Quantum Supersymmetric Models in the Causal Approach
NASA Astrophysics Data System (ADS)
Grigore, Dan-Radu
2007-04-01
We consider the massless supersymmetric vector multiplet in a purely quantum framework. First order gauge invariance determines uniquely the interaction Lagrangian as in the case of Yang-Mills models. Going to the second order of perturbation theory produces an anomaly which cannot be eliminated. We make the analysis of the model working only with the component fields.
Wess-Zumino model in the causal approach
NASA Astrophysics Data System (ADS)
Grigore, D. R.
2001-07-01
The Wess Zumino model is analysed in the framework of the causal approach of Epstein Glaser. The condition of invariance with respect to supersymmetry transformations is similar to gauge invariance in the Zürich formulation. We prove that this invariance condition can be implemented in all orders of perturbation theory, i.e. the anomalies are absent in all orders. This result is of a purely algebraic nature. We work consistently in the quantum framework based on the Bogoliubov axioms of perturbation theory, so no Grassmann variables are necessary.
A developmental approach to learning causal models for cyber security
NASA Astrophysics Data System (ADS)
Mugan, Jonathan
2013-05-01
To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.
A Novel Approach for Identifying Causal Models of Complex Diseases from Family Data
Park, Leeyoung; Kim, Ju H.
2015-01-01
Causal models including genetic factors are important for understanding the presentation mechanisms of complex diseases. Familial aggregation and segregation analyses based on polygenic threshold models have been the primary approach to fitting genetic models to the family data of complex diseases. In the current study, an advanced approach to obtaining appropriate causal models for complex diseases based on the sufficient component cause (SCC) model involving combinations of traditional genetics principles was proposed. The probabilities for the entire population, i.e., normal–normal, normal–disease, and disease–disease, were considered for each model for the appropriate handling of common complex diseases. The causal model in the current study included the genetic effects from single genes involving epistasis, complementary gene interactions, gene–environment interactions, and environmental effects. Bayesian inference using a Markov chain Monte Carlo algorithm (MCMC) was used to assess of the proportions of each component for a given population lifetime incidence. This approach is flexible, allowing both common and rare variants within a gene and across multiple genes. An application to schizophrenia data confirmed the complexity of the causal factors. An analysis of diabetes data demonstrated that environmental factors and gene–environment interactions are the main causal factors for type II diabetes. The proposed method is effective and useful for identifying causal models, which can accelerate the development of efficient strategies for identifying causal factors of complex diseases. PMID:25701286
Exploring manual asymmetries during grasping: a dynamic causal modeling approach
Begliomini, Chiara; Sartori, Luisa; Miotto, Diego; Stramare, Roberto; Motta, Raffaella; Castiello, Umberto
2015-01-01
Recording of neural activity during grasping actions in macaques showed that grasp-related sensorimotor transformations are accomplished in a circuit constituted by the anterior part of the intraparietal sulcus (AIP), the ventral (F5) and the dorsal (F2) region of the premotor area. In humans, neuroimaging studies have revealed the existence of a similar circuit, involving the putative homolog of macaque areas AIP, F5, and F2. These studies have mainly considered grasping movements performed with the right dominant hand and only a few studies have measured brain activity associated with a movement performed with the left non-dominant hand. As a consequence of this gap, how the brain controls for grasping movement performed with the dominant and the non-dominant hand still represents an open question. A functional magnetic resonance imaging (fMRI) experiment has been conducted, and effective connectivity (dynamic causal modeling, DCM) was used to assess how connectivity among grasping-related areas is modulated by hand (i.e., left and right) during the execution of grasping movements toward a small object requiring precision grasping. Results underlined boosted inter-hemispheric couplings between dorsal premotor cortices during the execution of movements performed with the left rather than the right dominant hand. More specifically, they suggest that the dorsal premotor cortices may play a fundamental role in monitoring the configuration of fingers when grasping movements are performed by either the right and the left hand. This role becomes particularly evident when the hand less-skilled (i.e., the left hand) to perform such action is utilized. The results are discussed in light of recent theories put forward to explain how parieto-frontal connectivity is modulated by the execution of prehensile movements. PMID:25759677
NASA Astrophysics Data System (ADS)
Costa, Fabio; Shrapnel, Sally
2016-06-01
Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces ‘spooky’ hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of classical causal modelling techniques, including the causal Markov condition and faithfulness. Based on the process matrix formalism, this framework naturally extends to generalised structures with indefinite causal order.
Ge, Tian; Kendrick, Keith M.; Feng, Jianfeng
2009-01-01
Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning. PMID:19936225
A causal model of positive health practices: the relationship between approach and replication.
Yarcheski, A; Mahon, N E
1989-01-01
This study supports the position that causal models developed a priori preclude replication with varied samples. Based on a critique of a study of positive health practices among adults (Muhlenkamp & Sayles, 1986), a causal model of positive health practices for adolescents was developed a priori from a theoretical formulation. Using data from a sample of 165 adolescents who responded to the Personal Lifestyle Questionnaire, the Rosenberg Self-Esteem Scale, Part 2 of the Personal Resource Questionnaire which measures a social support system, and a demographic data sheet, the intercorrelations among the study variables were analyzed using correlation coefficients. The causal model was then tested with the adolescent data using the LISREL VI program. The results showed a relatively good fit of the model to the data via a number of indicators. The model was then applied to data published from adults (Muhlenkamp & Sayles) using the LISREL VI program. The results indicated that there was a relatively poor fit of the model to the adult data, thus demonstrating the problem of replicating causal models with varied samples when the correct approach to causal modeling is used. The discussion focuses on theoretical and methodological reasons for the findings. PMID:2928152
Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)
NASA Astrophysics Data System (ADS)
Lozano, A. C.
2010-12-01
Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring
Review of aerospace engineering cost modelling: The genetic causal approach
NASA Astrophysics Data System (ADS)
Curran, R.; Raghunathan, S.; Price, M.
2004-11-01
The primary intention of this paper is to review the current state of the art in engineering cost modelling as applied to aerospace. This is a topic of current interest and in addressing the literature, the presented work also sets out some of the recognised definitions of cost that relate to the engineering domain. The paper does not attempt to address the higher-level financial sector but rather focuses on the costing issues directly relevant to the engineering process, primarily those of design and manufacture. This is of more contemporary interest as there is now a shift towards the analysis of the influence of cost, as defined in more engineering related terms; in an attempt to link into integrated product and process development (IPPD) within a concurrent engineering environment. Consequently, the cost definitions are reviewed in the context of the nature of cost as applicable to the engineering process stages: from bidding through to design, to manufacture, to procurement and ultimately, to operation. The linkage and integration of design and manufacture is addressed in some detail. This leads naturally to the concept of engineers influencing and controlling cost within their own domain rather than trusting this to financers who have little control over the cause of cost. In terms of influence, the engineer creates the potential for cost and in a concurrent environment this requires models that integrate cost into the decision making process.
Tonglet, R.; Mudosa, M.; Badashonderana, M.; Beghin, I.; Hennart, P.
1992-01-01
Reported are the results of a case study from Kirotshe rural health district, Northern Kivu, Zaire, where a workshop on the causal model approach to nutrition was organized in 1987. The model has since been used in the field for research design, training of health professionals, nutrition intervention, and community development. The rationale behind this approach is reviewed, the experience accumulated from Kirotshe district is described, and the ways in which the causal model contributes to comprehensive health and nutrition care are discussed. The broad range of possible policy implications of this approach underlines its usefulness for future action. PMID:1486667
ERIC Educational Resources Information Center
Watt, James H., Jr.
Pointing out that linear causal models can organize the interrelationships of a large number of variables, this paper contends that such models are particularly useful to mass communication research, which must by necessity deal with complex systems of variables. The paper first outlines briefly the philosophical requirements for establishing a…
A conditional Granger causality model approach for group analysis in functional MRI
Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun
2011-01-01
Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve
A Causal Modeling Approach to the Analysis of Course Evaluation Data.
ERIC Educational Resources Information Center
Wolfe, Mary L.
Five causal models relating several aspects of end-of-term student evaluation of a graduate course in nursing research methods were proposed and tested empirically. The course evaluation form consisted of four Likert-type subscales, on which students rated the following aspects of the course: (1) the extent to which the course met its objectives;…
ERIC Educational Resources Information Center
Hardeman, Wendy; Sutton, Stephen; Griffin, Simon; Johnston, Marie; White, Anthony; Wareham, Nicholas J.; Kinmonth, Ann Louise
2005-01-01
Theory-based intervention programmes to support health-related behaviour change aim to increase health impact and improve understanding of mechanisms of behaviour change. However, the science of intervention development remains at an early stage. We present a causal modelling approach to developing complex interventions for evaluation in…
A Causal, Data-driven Approach to Modeling the Kepler Data
NASA Astrophysics Data System (ADS)
Wang, Dun; Hogg, David W.; Foreman-Mackey, Daniel; Schölkopf, Bernhard
2016-09-01
Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science—the most precise photometric measurements of stars ever made—appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here, we present the causal pixel model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM models the systematic effects in the time series of a pixel using the pixels of many other stars and the assumption that any shared signal in these causally disconnected light curves is caused by instrumental effects. In addition, we use the target star’s future and past (autoregression). By appropriately separating, for each data point, the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the model. The method has four tuning parameters—the number of predictor stars or pixels, the autoregressive window size, and two L2-regularization amplitudes for model components, which we set by cross-validation. We determine values for tuning parameters that works well for most of the stars and apply the method to a corresponding set of target stars. We find that CPM can consistently produce low-noise light curves. In this paper, we demonstrate that pixel-level de-trending is possible while retaining transit signals, and we think that methods like CPM are generally applicable and might be useful for K2, TESS, etc., where the data are not clean postage stamps like Kepler.
Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.
2013-01-01
Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726
Loop anomalies in the causal approach
NASA Astrophysics Data System (ADS)
Grigore, Dan-Radu
2015-01-01
We consider gauge models in the causal approach and study one-loop contributions to the chronological products and the anomalies they produce. We prove that in order greater than 4 there are no one-loop anomalies. Next we analyze one-loop anomalies in the second- and third-order of the perturbation theory. We prove that the even parity contributions (with respect to parity) do not produce anomalies; for the odd parity contributions we reobtain the well-known result.
NASA Astrophysics Data System (ADS)
Pearl, Judea
2000-03-01
Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
Granger causality for state-space models
NASA Astrophysics Data System (ADS)
Barnett, Lionel; Seth, Anil K.
2015-04-01
Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.
Effective connectivity: Influence, causality and biophysical modeling
Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl
2011-01-01
This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655
Analysing connectivity with Granger causality and dynamic causal modelling
Friston, Karl; Moran, Rosalyn; Seth, Anil K
2013-01-01
This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and — within functional integration — the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues. PMID:23265964
A General Approach to Causal Mediation Analysis
ERIC Educational Resources Information Center
Imai, Kosuke; Keele, Luke; Tingley, Dustin
2010-01-01
Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the…
Causal reasoning with mental models
Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.
2014-01-01
This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398
Causal reasoning with mental models.
Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N
2014-01-01
This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398
Causal Models of Literacy Acquisition.
ERIC Educational Resources Information Center
Goetz, Ernest T.; And Others
1992-01-01
Examines seven articles that employed path analysis to test causal models of the acquisition of literacy or the reading-writing relationship. Reveals that, although such analysis holds promise for a better understanding of the components of literacy, several potential difficulties remain for those attempting to synthesize this body of literature.…
A causal net approach to relativistic quantum mechanics
NASA Astrophysics Data System (ADS)
Bateson, R. D.
2012-05-01
In this paper we discuss a causal network approach to describing relativistic quantum mechanics. Each vertex on the causal net represents a possible point event or particle observation. By constructing the simplest causal net based on Reichenbach-like conjunctive forks in proper time we can exactly derive the 1+1 dimension Dirac equation for a relativistic fermion and correctly model quantum mechanical statistics. Symmetries of the net provide various quantum mechanical effects such as quantum uncertainty and wavefunction, phase, spin, negative energy states and the effect of a potential. The causal net can be embedded in 3+1 dimensions and is consistent with the conventional Dirac equation. In the low velocity limit the causal net approximates to the Schrodinger equation and Pauli equation for an electromagnetic field. Extending to different momentum states the net is compatible with the Feynman path integral approach to quantum mechanics that allows calculation of well known quantum phenomena such as diffraction.
Modeling of causality with metamaterials
NASA Astrophysics Data System (ADS)
Smolyaninov, Igor I.
2013-02-01
Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space-time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space-time. While this model may be used to build interesting space-time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space-time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler-Feynman absorber theory of causality.
Home Environment, Self-Concept, and Academic Achievement: A Causal Modeling Approach.
ERIC Educational Resources Information Center
Song, In-Sub; Hattie, John
1984-01-01
Structural equation modeling was used to investigate the relation between home environment, self-concept, and academic achievement. It was found and cross-validated over four samples of 2,297 Korean adolescents that self-concept is a mediating variable between home environment and academic achievement. (Author/BS)
The metagenomic approach and causality in virology
Castrignano, Silvana Beres; Nagasse-Sugahara, Teresa Keico
2015-01-01
Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease. PMID:25902566
A Complex Systems Approach to Causal Discovery in Psychiatry
Saxe, Glenn N.; Statnikov, Alexander; Fenyo, David; Ren, Jiwen; Li, Zhiguo; Prasad, Meera; Wall, Dennis; Bergman, Nora; Briggs, Ernestine C.; Aliferis, Constantin
2016-01-01
Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach–the Complex Systems-Causal Network (CS-CN) method–designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a ‘gold standard’ dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry. PMID:27028297
A Causal Model of Faculty Research Productivity.
ERIC Educational Resources Information Center
Bean, John P.
A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…
Porta, Alberto; Faes, Luca; Bari, Vlasta; Marchi, Andrea; Bassani, Tito; Nollo, Giandomenico; Perseguini, Natália Maria; Milan, Juliana; Minatel, Vinícius; Borghi-Silva, Audrey; Takahashi, Anielle C. M.; Catai, Aparecida M.
2014-01-01
The proposed approach evaluates complexity of the cardiovascular control and causality among cardiovascular regulatory mechanisms from spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiration (RESP). It relies on construction of a multivariate embedding space, optimization of the embedding dimension and a procedure allowing the selection of the components most suitable to form the multivariate embedding space. Moreover, it allows the comparison between linear model-based (MB) and nonlinear model-free (MF) techniques and between MF approaches exploiting local predictability (LP) and conditional entropy (CE). The framework was applied to study age-related modifications of complexity and causality in healthy humans in supine resting (REST) and during standing (STAND). We found that: 1) MF approaches are more efficient than the MB method when nonlinear components are present, while the reverse situation holds in presence of high dimensional embedding spaces; 2) the CE method is the least powerful in detecting age-related trends; 3) the association of HP complexity on age suggests an impairment of cardiac regulation and response to STAND; 4) the relation of SAP complexity on age indicates a gradual increase of sympathetic activity and a reduced responsiveness of vasomotor control to STAND; 5) the association from SAP to HP on age during STAND reveals a progressive inefficiency of baroreflex; 6) the reduced connection from HP to SAP with age might be linked to the progressive exploitation of Frank-Starling mechanism at REST and to the progressive increase of peripheral resistances during STAND; 7) at REST the diminished association from RESP to HP with age suggests a vagal withdrawal and a gradual uncoupling between respiratory activity and heart; 8) the weakened connection from RESP to SAP with age might be related to the progressive increase of left ventricular thickness and vascular stiffness and to the gradual decrease of
Spin foam models as energetic causal sets
NASA Astrophysics Data System (ADS)
Cortês, Marina; Smolin, Lee
2016-04-01
Energetic causal sets are causal sets endowed by a flow of energy-momentum between causally related events. These incorporate a novel mechanism for the emergence of space-time from causal relations [M. Cortês and L. Smolin, Phys. Rev. D 90, 084007 (2014); Phys. Rev. D 90, 044035 (2014)]. Here we construct a spin foam model which is also an energetic causal set model. This model is closely related to the model introduced in parallel by Wolfgang Wieland in [Classical Quantum Gravity 32, 015016 (2015)]. What makes a spin foam model also an energetic causal set is Wieland's identification of new degrees of freedom analogous to momenta, conserved at events (or four-simplices), whose norms are not mass, but the volume of tetrahedra. This realizes the torsion constraints, which are missing in previous spin foam models, and are needed to relate the connection dynamics to those of the metric, as in general relativity. This identification makes it possible to apply the new mechanism for the emergence of space-time to a spin foam model. Our formulation also makes use of Markopoulou's causal formulation of spin foams [arXiv:gr-qc/9704013]. These are generated by evolving spin networks with dual Pachner moves. This endows the spin foam history with causal structure given by a partial ordering of the events which are dual to four-simplices.
Perturbative gravity in the causal approach
NASA Astrophysics Data System (ADS)
Grigore, D. R.
2010-01-01
Quantum theory of the gravitation in the causal approach is studied up to the second order of perturbation theory in the causal approach. We emphasize the use of cohomology methods in this framework. After describing in detail the mathematical structure of the cohomology method we apply it in three different situations: (a) the determination of the most general expression of the interaction Lagrangian; (b) the proof of gauge invariance in the second order of perturbation theory for the pure gravity system—massless and massive; (c) the investigation of the arbitrariness of the second-order chronological products compatible with renormalization principles and gauge invariance (i.e. the renormalization problem in the second order of perturbation theory). In case (a) we investigate pure gravity systems and the interaction of massless gravity with matter (described by scalars and spinors) and massless Yang-Mills fields. We obtain a difference with respect to the classical field theory due to the fact that in quantum field theory one cannot enforce the divergenceless property on the vector potential and this spoils the divergenceless property of the usual energy-momentum tensor. To correct this one needs a supplementary ghost term in the interaction Lagrangian. In all three case, the computations are more simple than by the usual methods.
Compact Representations of Extended Causal Models
ERIC Educational Resources Information Center
Halpern, Joseph Y.; Hitchcock, Christopher
2013-01-01
Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of "normality." In Halpern and Hitchcock (2011), we offer a definition of actual causation…
ERIC Educational Resources Information Center
Liang, Ling L.; Fulmer, Gavin W.; Majerich, David M.; Clevenstine, Richard; Howanski, Raymond
2012-01-01
The purpose of this study is to examine the effects of a model-based introductory physics curriculum on conceptual learning in a Physics First (PF) Initiative. This is the first comparative study in physics education that applies the Rasch modeling approach to examine the effects of a model-based curriculum program combined with PF in the United…
NASA Astrophysics Data System (ADS)
Liang, Ling L.; Fulmer, Gavin W.; Majerich, David M.; Clevenstine, Richard; Howanski, Raymond
2012-02-01
The purpose of this study is to examine the effects of a model-based introductory physics curriculum on conceptual learning in a Physics First (PF) Initiative. This is the first comparative study in physics education that applies the Rasch modeling approach to examine the effects of a model-based curriculum program combined with PF in the United States. Five teachers and 301 students (in grades 9 through 12) in two mid-Atlantic high schools participated in the study. The students' conceptual learning was measured by the Force Concept Inventory (FCI). It was found that the ninth-graders enrolled in the model-based program in a PF initiative achieved substantially greater conceptual understanding of the physics content than those 11th-/12th-graders enrolled in the conventional non-modeling, non-PF program (Honors strand). For the 11th-/12th-graders enrolled in the non-PF, non-honors strands, the modeling classes also outperformed the conventional non-modeling classes. The instructional activity reports by students indicated that the model-based approach was generally implemented in modeling classrooms. A closer examination of the field notes and the classroom observation profiles revealed that the greatest inconsistencies in model-based teaching practices observed were related to classroom interactions or discourse. Implications and recommendations for future studies are also discussed.
Nonlinear parametric model for Granger causality of time series
NASA Astrophysics Data System (ADS)
Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano
2006-06-01
The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.
Model Averaging for Improving Inference from Causal Diagrams
Hamra, Ghassan B.; Kaufman, Jay S.; Vahratian, Anjel
2015-01-01
Model selection is an integral, yet contentious, component of epidemiologic research. Unfortunately, there remains no consensus on how to identify a single, best model among multiple candidate models. Researchers may be prone to selecting the model that best supports their a priori, preferred result; a phenomenon referred to as “wish bias”. Directed acyclic graphs (DAGs), based on background causal and substantive knowledge, are a useful tool for specifying a subset of adjustment variables to obtain a causal effect estimate. In many cases, however, a DAG will support multiple, sufficient or minimally-sufficient adjustment sets. Even though all of these may theoretically produce unbiased effect estimates they may, in practice, yield somewhat distinct values, and the need to select between these models once again makes the research enterprise vulnerable to wish bias. In this work, we suggest combining adjustment sets with model averaging techniques to obtain causal estimates based on multiple, theoretically-unbiased models. We use three techniques for averaging the results among multiple candidate models: information criteria weighting, inverse variance weighting, and bootstrapping. We illustrate these approaches with an example from the Pregnancy, Infection, and Nutrition (PIN) study. We show that each averaging technique returns similar, model averaged causal estimates. An a priori strategy of model averaging provides a means of integrating uncertainty in selection among candidate, causal models, while also avoiding the temptation to report the most attractive estimate from a suite of equally valid alternatives. PMID:26270672
Dynamic causal models and autopoietic systems.
David, Olivier
2007-01-01
Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models) for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated. PMID:18575681
Exploring Causal Models of Educational Achievement.
ERIC Educational Resources Information Center
Parkerson, Jo Ann; And Others
1984-01-01
This article evaluates five causal model of educational productivity applied to learning science in a sample of 882 fifth through eighth graders. Each model explores the relationship between achievement and a combination of eight constructs: home environment, peer group, media, ability, social environment, time on task, motivation, and…
Causality, Confirmation, Credulity, and Structural Equation Modeling.
ERIC Educational Resources Information Center
Biddle, Bruce J.; Marlin, Marjorie M.
1987-01-01
Defines structural equation modeling (SEM) and points out its relation to other more familiar data-analytic techniques, as well as some of the potentials and pitfalls of SEM in the analysis of developmental data. Discussion focuses on causal modeling, path diagrams, ordinary least-squares regression analysis, and powerful methods for model…
A Quantum Probability Model of Causal Reasoning
Trueblood, Jennifer S.; Busemeyer, Jerome R.
2012-01-01
People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747
A Causal Model of Faculty Turnover Intentions.
ERIC Educational Resources Information Center
Smart, John C.
1990-01-01
A causal model assesses the relative influence of individual attributes, institutional characteristics, contextual-work environment variables, and multiple measures of job satisfaction on faculty intentions to leave their current institutions. Factors considered include tenure status, age, institutional status, governance style, organizational…
NASA Astrophysics Data System (ADS)
Ebert-Uphoff, I.; Hammerling, D.; Samarasinghe, S.; Baker, A. H.
2015-12-01
The framework of causal discovery provides algorithms that seek to identify potential cause-effect relationships from observational data. The output of such algorithms is a graph structure that indicates the potential causal connections between the observed variables. Originally developed for applications in the social sciences and economics, causal discovery has been used with great success in bioinformatics and, most recently, in climate science, primarily to identify interaction patterns between compound climate variables and to track pathways of interactions between different locations around the globe. Here we apply causal discovery to the output data of climate models to learn so-called causal signatures from the data that indicate interactions between the different atmospheric variables. These causal signatures can act like fingerprints for the underlying dynamics and thus serve a variety of diagnostic purposes. We study the use of the causal signatures for three applications: 1) For climate model software verification we suggest to use causal signatures as a means of detecting statistical differences between model runs, thus identifying potential errors and supplementing the Community Earth System Model Ensemble Consistency Testing (CESM-ECT) tool recently developed at NCAR for CESM verification. 2) In the context of data compression of model runs, we will test how much the causal signatures of the model outputs changes after different compression algorithms have been applied. This may result in additional means to determine which type and amount of compression is acceptable. 3) This is the first study applying causal discovery simultaneously to a large number of different atmospheric variables, and in the process of studying the resulting interaction patterns for the two aforementioned applications, we expect to gain some new insights into their relationships from this approach. We will present first results obtained for Applications 1 and 2 above.
Subjective spacetime derived from a causal histories approach
NASA Astrophysics Data System (ADS)
Gunji, Yukio-Pegio; Haruna, Taichi; Uragami, Daisuke; Nishikawa, Asaki
2009-10-01
The internal description of spacetime can reveal ambiguity regarding an observer’s perception of the present, where an observer can refer to the present as if he were outside spacetime while actually existing in the present. This ambiguity can be expressed as the compatibility between an element and a set, and is here called a/{a}-compatibility. We describe a causal set as a lattice and a causal history as a quotient lattice, and implement the a/{a}-compatibility in the framework of a causal histories approach. This leads to a perpetual change of a pair of causal set and causal history, and can be used to describe subjective spacetime including the déjà vu experience and/or schizophrenic time.
Causality in Psychiatry: A Hybrid Symptom Network Construct Model.
Young, Gerald
2015-01-01
Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639
Causality in Psychiatry: A Hybrid Symptom Network Construct Model
Young, Gerald
2015-01-01
Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639
The Specification of Causal Models with Tetrad IV: A Review
ERIC Educational Resources Information Center
Landsheer, J. A.
2010-01-01
Tetrad IV is a program designed for the specification of causal models. It is specifically designed to search for causal relations, but also offers the possibility to estimate the parameters of a structural equation model. It offers a remarkable graphical user interface, which facilitates building, evaluating, and searching for causal models. The…
A Quantitative Causal Model Theory of Conditional Reasoning
ERIC Educational Resources Information Center
Fernbach, Philip M.; Erb, Christopher D.
2013-01-01
The authors propose and test a causal model theory of reasoning about conditional arguments with causal content. According to the theory, the acceptability of modus ponens (MP) and affirming the consequent (AC) reflect the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Acceptability…
MacKinnon, David P.; Pirlott, Angela G.
2016-01-01
Statistical mediation methods provide valuable information about underlying mediating psychological processes, but the ability to infer that the mediator variable causes the outcome variable is more complex than widely known. Researchers have recently emphasized how violating assumptions about confounder bias severely limits causal inference of the mediator to dependent variable relation. Our article describes and addresses these limitations by drawing on new statistical developments in causal mediation analysis. We first review the assumptions underlying causal inference and discuss three ways to examine the effects of confounder bias when assumptions are violated. We then describe four approaches to address the influence of confounding variables and enhance causal inference, including comprehensive structural equation models, instrumental variable methods, principal stratification, and inverse probability weighting. Our goal is to further the adoption of statistical methods to enhance causal inference in mediation studies. PMID:25063043
Hung-Pin, Lin
2014-01-01
The purpose of this paper is to investigate the short-run and long-run causality between renewable energy (RE) consumption and economic growth (EG) in nine OECD countries from the period between 1982 and 2011. To examine the linkage, this paper uses the autoregressive distributed lag (ARDL) bounds testing approach of cointegration test and vector error-correction models to test the causal relationship between variables. The co-integration and causal relationships are found in five countries—United States of America (USA), Japan, Germany, Italy, and United Kingdom (UK). The overall results indicate that (1) a short-run unidirectional causality runs from EG to RE in Italy and UK; (2) long-run unidirectional causalities run from RE to EG for Germany, Italy, and UK; (3) a long-run unidirectional causality runs from EG to RE in USA, and Japan; (4) both long-run and strong unidirectional causalities run from RE to EG for Germany and UK; and (5) Finally, both long-run and strong unidirectional causalities run from EG to RE in only USA. Further evidence reveals that policies for renewable energy conservation may have no impact on economic growth in France, Denmark, Portugal, and Spain. PMID:24558343
Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory
ERIC Educational Resources Information Center
Gopnik, Alison; Wellman, Henry M.
2012-01-01
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…
A causal model of adolescent depression.
Brage, D; Meredith, W
1994-07-01
We examined how family strengths, parent-adolescent communication, self-esteem, loneliness, age, and gender interrelate, and how this interaction influences depression in adolescents. The data were collected on a written questionnaire completed by 156 adolescents who were attending public schools in four communities in the midwestern United States. We developed a causal model to explicate the relationships among the variables hypothesized to affect adolescent depression and analyzed the data using path analysis via the LISREL VII program. Results showed a good fit of the model to the data. Loneliness and self-esteem had a direct effect on adolescent depression. Self-esteem had an indirect effect on depression through loneliness. Age directly and indirectly influenced depression through loneliness. Gender was significantly related to depression through self-esteem. Family strengths indirectly affected depression through self-esteem. PMID:7932297
The structure of the anomalies of gauge theories in the causal approach
NASA Astrophysics Data System (ADS)
Grigore, D. R.
2002-02-01
We consider the gauge invariance of the standard Yang-Mills model in the framework of the causal approach of Epstein-Glaser and Scharf and determine the generic form of the anomalies. The method used is based on the Epstein-Glaser approach to renormalization theory. In the case of quantum electrodynamics we obtain quite easily the absence of anomalies in all orders.
Hindsight bias and causal reasoning: a minimalist approach.
Yopchick, Jennelle E; Kim, Nancy S
2012-02-01
What factors contribute to hindsight bias, the phenomenon whereby the known outcome of an event appears obvious only after the fact? The Causal Model Theory (CMT) of hindsight bias (Nestler et al. in Soc Psychol 39:182-188, 2008a; in J Expl Psychol: Learn Mem Cog 34:1043-1054, 2008b; Pezzo in Mem 11:421-441, 2003; Wasserman et al. in Pers Soc Psychol Bull 17:30-35, 1991) posits that hindsight bias can occur when people have the opportunity to identify potential causal antecedents and evaluate whether they could have led to the outcome. Two experiments incorporating highly controlled minimalist scenarios supported the CMT. As predicted by the CMT, hindsight bias occurred when the causal factor explained the actual outcome better than the alternative outcome, and reverse hindsight bias occurred when the causal factor explained the alternative outcome better than the actual outcome. Moreover, we found new evidence that outcome knowledge alone was insufficient to elicit hindsight bias in the absence of a potential causal antecedent. Implications for future directions in hindsight bias research are discussed. PMID:21912981
ERIC Educational Resources Information Center
Markus, Keith A.
2008-01-01
One can distinguish statistical models used in causal modeling from the causal interpretations that align them with substantive hypotheses. Causal modeling typically assumes an efficient causal interpretation of the statistical model. Causal modeling can also make use of mereological causal interpretations in which the state of the parts…
Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy
ERIC Educational Resources Information Center
von Eye, Alexander; Wiedermann, Wolfgang
2015-01-01
Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…
A Causal Model of Factors Influencing Faculty Use of Technology
ERIC Educational Resources Information Center
Meyer, Katrina A.; Xu, Yonghong Jade
2009-01-01
Based on earlier studies using the 1999 and 2004 National Study of Postsecondary Faculty (NSOPF) data [1, 2], a causal model explaining faculty technology use was constructed. Path analysis was used to test the causal effects of age, gender, highest degree, discipline (health science or not), recent research productivity, and teaching load on…
Causal Inferences with Group Based Trajectory Models
ERIC Educational Resources Information Center
Haviland, Amelia M.; Nagin, Daniel S.
2005-01-01
A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This paper lays out and applies a method for using observational longitudinal data to make more confident causal inferences about the…
Causal interpretation rules for encoding and decoding models in neuroimaging.
Weichwald, Sebastian; Meyer, Timm; Özdenizci, Ozan; Schölkopf, Bernhard; Ball, Tonio; Grosse-Wentrup, Moritz
2015-04-15
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and decoding models is not sufficient for this purpose: relevant features in encoding and decoding models carry a different meaning in stimulus- and in response-based experimental paradigms.We show that only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models trained on the same data, however, we obtain insights into causal relations beyond those that are implied by each individual model type. We illustrate the empirical relevance of our theoretical findings on EEG data recorded during a visuo-motor learning task. PMID:25623501
Causality and quantum criticality in long-range lattice models
NASA Astrophysics Data System (ADS)
Maghrebi, Mohammad F.; Gong, Zhe-Xuan; Foss-Feig, Michael; Gorshkov, Alexey V.
2016-03-01
Long-range quantum lattice systems often exhibit drastically different behavior than their short-range counterparts. In particular, because they do not satisfy the conditions for the Lieb-Robinson theorem, they need not have an emergent relativistic structure in the form of a light cone. Adopting a field-theoretic approach, we study the one-dimensional transverse-field Ising model with long-range interactions, and a fermionic model with long-range hopping and pairing terms, explore their critical and near-critical behavior, and characterize their response to local perturbations. We deduce the dynamic critical exponent, up to the two-loop order within the renormalization group theory, which we then use to characterize the emergent causal behavior. We show that beyond a critical value of the power-law exponent of the long-range couplings, the dynamics effectively becomes relativistic. Various other critical exponents describing correlations in the ground state, as well as deviations from a linear causal cone, are deduced for a wide range of the power-law exponent.
Causal mediation analyses with rank preserving models.
Have, Thomas R Ten; Joffe, Marshall M; Lynch, Kevin G; Brown, Gregory K; Maisto, Stephen A; Beck, Aaron T
2007-09-01
We present a linear rank preserving model (RPM) approach for analyzing mediation of a randomized baseline intervention's effect on a univariate follow-up outcome. Unlike standard mediation analyses, our approach does not assume that the mediating factor is also randomly assigned to individuals in addition to the randomized baseline intervention (i.e., sequential ignorability), but does make several structural interaction assumptions that currently are untestable. The G-estimation procedure for the proposed RPM represents an extension of the work on direct effects of randomized intervention effects for survival outcomes by Robins and Greenland (1994, Journal of the American Statistical Association 89, 737-749) and on intervention non-adherence by Ten Have et al. (2004, Journal of the American Statistical Association 99, 8-16). Simulations show good estimation and confidence interval performance by the proposed RPM approach under unmeasured confounding relative to the standard mediation approach, but poor performance under departures from the structural interaction assumptions. The trade-off between these assumptions is evaluated in the context of two suicide/depression intervention studies. PMID:17825022
Effects of question formats on causal judgments and model evaluation
Smithson, Michael
2015-01-01
Evaluation of causal reasoning models depends on how well the subjects’ causal beliefs are assessed. Elicitation of causal beliefs is determined by the experimental questions put to subjects. We examined the impact of question formats commonly used in causal reasoning research on participant’s responses. The results of our experiment (Study 1) demonstrate that both the mean and homogeneity of the responses can be substantially influenced by the type of question (structure induction versus strength estimation versus prediction). Study 2A demonstrates that subjects’ responses to a question requiring them to predict the effect of a candidate cause can be significantly lower and more heterogeneous than their responses to a question asking them to diagnose a cause when given an effect. Study 2B suggests that diagnostic reasoning can strongly benefit from cues relating to temporal precedence of the cause in the question. Finally, we evaluated 16 variations of recent computational models and found the model fitting was substantially influenced by the type of questions. Our results show that future research in causal reasoning should place a high priority on disentangling the effects of question formats from the effects of experimental manipulations, because that will enable comparisons between models of causal reasoning uncontaminated by method artifact. PMID:25954225
The Mental Representation of Causal Conditional Reasoning: Mental Models or Causal Models
ERIC Educational Resources Information Center
Ali, Nilufa; Chater, Nick; Oaksford, Mike
2011-01-01
In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving "pairs" of conditionals--such as "if P[subscript 1] then Q" and "if P[subscript…
Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid
2015-12-01
This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions. PMID:26282441
ERIC Educational Resources Information Center
Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio
2016-01-01
We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…
NASA Astrophysics Data System (ADS)
Sizochenko, Natalia; Gajewicz, Agnieszka; Leszczynski, Jerzy; Puzyn, Tomasz
2016-03-01
In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase ``correlation does not imply causation'' reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal
Developing a Causal Model from Liver Function Test Data
NASA Astrophysics Data System (ADS)
Inada, Masanori; Terano, Takao
As Active Mining is a new concept among data mining and/or knowledge discovery in databases communities, in order to validate the effectiveness, it is important to carry out empirical studies using practical data. Based on the concept of Active User Reaction, this paper develops a causal model from liver function test data in a medical domain. To develop the model, we have set a problem to predict the values of ICG (indocyanine green) test from given observation data and experts' background knowledge. We therefore employ a framework of meta-learning and structural equation modeling. In this paper meta-learning means learning about mined results from multiple data-mining techniques. Structural equation modeling enables us to describe flexible models from background knowledge. The construction of the causal model contains two phases: meta-learning and the model building. The meta-learning phase utilizes both the linear regression and the neural network as data mining techniques, then examines the predictability on the given data set. Mining models are n-folded learned from the training data set. Each of the prediction accuracy of the mining models is compared using with the testing data. On the model building phase, we use structural equation modeling to develop a causal model based on results of meta-learning and background knowledge. We again compare the accuracy of the causal model with each of the mining models. Consequently we have developed the causal model, which is comprehensible and have good predictive performance, via the meta-learning phase. Through the empirical study, we have got the conclusion that the framework of meta-learning is effective in data mining in a difficult medical domain.
Enhancing scientific reasoning by refining students' models of multivariable causality
NASA Astrophysics Data System (ADS)
Keselman, Alla
Inquiry learning as an educational method is gaining increasing support among elementary and middle school educators. In inquiry activities at the middle school level, students are typically asked to conduct investigations and infer causal relationships about multivariable causal systems. In these activities, students usually demonstrate significant strategic weaknesses and insufficient metastrategic understanding of task demands. Present work suggests that these weaknesses arise from students' deficient mental models of multivariable causality, in which effects of individual features are neither additive, nor constant. This study is an attempt to develop an intervention aimed at enhancing scientific reasoning by refining students' models of multivariable causality. Three groups of students engaged in a scientific investigation activity over seven weekly sessions. By creating unique combinations of five features potentially involved in earthquake mechanism and observing associated risk meter readings, students had to find out which of the features were causal, and to learn to predict earthquake risk. Additionally, students in the instructional and practice groups engaged in self-directed practice in making scientific predictions. The instructional group also participated in weekly instructional sessions on making predictions based on multivariable causality. Students in the practice and instructional conditions showed small to moderate improvement in their attention to the evidence and in their metastrategic ability to recognize effective investigative strategies in the work of other students. They also demonstrated a trend towards making a greater number of valid inferences than the control group students. Additionally, students in the instructional condition showed significant improvement in their ability to draw inferences based on multiple records. They also developed more accurate knowledge about non-causal features of the system. These gains were maintained
Causal Indicator Models: Unresolved Issues of Construction and Evaluation
ERIC Educational Resources Information Center
West, Stephen G.; Grimm, Kevin J.
2014-01-01
These authors agree with Bainter and Bollen that causal effects represents a useful measurement structure in some applications. The structure of the science of the measurement problem should determine the model; the measurement model should not determine the science. They also applaud Bainter and Bollen's important reminder that the full…
The TETRAD Project: Constraint Based Aids to Causal Model Specification.
ERIC Educational Resources Information Center
Scheines, Richard; Spirtes, Peter; Glymour, Clark; Meek, Christopher; Richardson, Thomas
1998-01-01
The TETRAD for constraint-based aids to causal model specification project and related work in computer science aims to apply standards of rigor and precision to the problem of using data and background knowledge to make inferences about a model's specifications. Several algorithms that are implemented in the TETRAD II program are presented. (SLD)
What Is the Latent Variable in Causal Indicator Models?
ERIC Educational Resources Information Center
Howell, Roy D.
2014-01-01
Building on the work of Bollen (2007) and Bollen & Bauldry (2011), Bainter and Bollen (this issue) clarifies several points of confusion in the literature regarding causal indicator models. This author would certainly agree that the effect indicator (reflective) measurement model is inappropriate for some indicators (such as the social…
Models of Causation and the Semantics of Causal Verbs
ERIC Educational Resources Information Center
Wolff, Phillip; Song, Grace
2003-01-01
This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb "cause" itself. According to focal set models of causation ([Cheng (1997]; [Cheng and Novick (1991 and Cheng and Novick (1992]), the concept of CAUSE should be more…
Causal Discovery of Dynamic Systems
ERIC Educational Resources Information Center
Voortman, Mark
2010-01-01
Recently, several philosophical and computational approaches to causality have used an interventionist framework to clarify the concept of causality [Spirtes et al., 2000, Pearl, 2000, Woodward, 2005]. The characteristic feature of the interventionist approach is that causal models are potentially useful in predicting the effects of manipulations.…
Nonlinear Dynamic Causal Models for fMRI
Stephan, Klaas Enno; Kasper, Lars; Harrison, Lee M.; Daunizeau, Jean; den Ouden, Hanneke E.M.; Breakspear, Michael; Friston, Karl J.
2009-01-01
Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i.e., how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e.g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e.g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1→V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both
Causal Model Progressions as a Foundation for Intelligent Learning Environments.
ERIC Educational Resources Information Center
White, Barbara Y.; Frederiksen, John R.
This paper describes the theoretical underpinnings and architecture of a new type of learning environment that incorporates features of microworlds and of intelligent tutoring systems. The environment is based on a progression of increasingly sophisticated causal models that simulate domain phenomena, generate explanations, and serve as student…
Nanoparticles in the environment: assessment using the causal diagram approach.
Smita, Suchi; Gupta, Shailendra K; Bartonova, Alena; Dusinska, Maria; Gutleb, Arno C; Rahman, Qamar
2012-01-01
Nanoparticles (NPs) cause concern for health and safety as their impact on the environment and humans is not known. Relatively few studies have investigated the toxicological and environmental effects of exposure to naturally occurring NPs (NNPs) and man-made or engineered NPs (ENPs) that are known to have a wide variety of effects once taken up into an organism. A review of recent knowledge (between 2000-2010) on NP sources, and their behaviour, exposure and effects on the environment and humans was performed. An integrated approach was used to comprise available scientific information within an interdisciplinary logical framework, to identify knowledge gaps and to describe environment and health linkages for NNPs and ENPs. The causal diagram has been developed as a method to handle the complexity of issues on NP safety, from their exposure to the effects on the environment and health. It gives an overview of available scientific information starting with common sources of NPs and their interactions with various environmental processes that may pose threats to both human health and the environment. Effects of NNPs on dust cloud formation and decrease in sunlight intensity were found to be important environmental changes with direct and indirect implication in various human health problems. NNPs and ENPs exposure and their accumulation in biological matrices such as microbiota, plants and humans may result in various adverse effects. The impact of some NPs on human health by ROS generation was found to be one of the major causes to develop various diseases. A proposed cause-effects diagram for NPs is designed considering both NNPs and ENPs. It represents a valuable information package and user-friendly tool for various stakeholders including students, researchers and policy makers, to better understand and communicate on issues related to NPs. PMID:22759495
Nanoparticles in the environment: assessment using the causal diagram approach
2012-01-01
Nanoparticles (NPs) cause concern for health and safety as their impact on the environment and humans is not known. Relatively few studies have investigated the toxicological and environmental effects of exposure to naturally occurring NPs (NNPs) and man-made or engineered NPs (ENPs) that are known to have a wide variety of effects once taken up into an organism. A review of recent knowledge (between 2000-2010) on NP sources, and their behaviour, exposure and effects on the environment and humans was performed. An integrated approach was used to comprise available scientific information within an interdisciplinary logical framework, to identify knowledge gaps and to describe environment and health linkages for NNPs and ENPs. The causal diagram has been developed as a method to handle the complexity of issues on NP safety, from their exposure to the effects on the environment and health. It gives an overview of available scientific information starting with common sources of NPs and their interactions with various environmental processes that may pose threats to both human health and the environment. Effects of NNPs on dust cloud formation and decrease in sunlight intensity were found to be important environmental changes with direct and indirect implication in various human health problems. NNPs and ENPs exposure and their accumulation in biological matrices such as microbiota, plants and humans may result in various adverse effects. The impact of some NPs on human health by ROS generation was found to be one of the major causes to develop various diseases. A proposed cause-effects diagram for NPs is designed considering both NNPs and ENPs. It represents a valuable information package and user-friendly tool for various stakeholders including students, researchers and policy makers, to better understand and communicate on issues related to NPs. PMID:22759495
Modeling the Causal Regulation of Transversely Accelerated Ion (TAI) Outflows
NASA Astrophysics Data System (ADS)
Varney, R. H.; Wiltberger, M. J.; Zhang, B.; Schmitt, P.; Lotko, W.
2013-12-01
TAIs are generated by wave particle interactions driven by waves at temporal and spatial scales which are inaccessible in global coupled geospace models. So far attempts to include TAI outflows in global models have focused on the use of empirical correlations between observed outflow fluxes and various inputs such as DC Poynting flux, Alfvénic Poynting flux, and electron precipitation fluxes. These treatments ignore feedbacks between the outflow and the state of the ionosphere and assume the spatial and temporal distributions of the outflows are identical to those of their drivers. This work presents an alternative approach which can overcome these deficiencies while still being sufficiently computationally efficient to couple into a global modeling framework. TAIs are incorporated into a 3-D fluid model of the ionosphere and polar wind by modeling them as a separate fluid which obeys transport equations appropriate for monoenergetic conic distributions. The characteristics of the TAI outflow produced depend on the assumed transverse heating rates and the 'promotion rate' which connects the TAI fluid to the thermal O+ fluid. Using drivers extracted from runs of the Coupled Magnetosphere Ionosphere Thermosphere (CMIT) model, different strategies for causally regulating these free parameters are explored. The model can reproduce many of the observed features of TAI outflows but also exhibits physical attributes that empirical relationships alone miss. These characteristics include flux limiting of the outflow from below when intense outflow creates high-altitude cavities, time delays between the onset of transverse heating and the appearance of outflow, and spatial distributions of outflow which are different from the spatial distributions of the applied transverse heating and which depend on the ionospheric convection pattern.
Causal Models for Mediation Analysis: An Introduction to Structural Mean Models.
Zheng, Cheng; Atkins, David C; Zhou, Xiao-Hua; Rhew, Isaac C
2015-01-01
Mediation analyses are critical to understanding why behavioral interventions work. To yield a causal interpretation, common mediation approaches must make an assumption of "sequential ignorability." The current article describes an alternative approach to causal mediation called structural mean models (SMMs). A specific SMM called a rank-preserving model (RPM) is introduced in the context of an applied example. Particular attention is given to the assumptions of both approaches to mediation. Applying both mediation approaches to the college student drinking data yield notable differences in the magnitude of effects. Simulated examples reveal instances in which the traditional approach can yield strongly biased results, whereas the RPM approach remains unbiased in these cases. At the same time, the RPM approach has its own assumptions that must be met for correct inference, such as the existence of a covariate that strongly moderates the effect of the intervention on the mediator and no unmeasured confounders that also serve as a moderator of the effect of the intervention or the mediator on the outcome. The RPM approach to mediation offers an alternative way to perform mediation analysis when there may be unmeasured confounders. PMID:26717122
ERIC Educational Resources Information Center
Perkins, David N.; Grotzer, Tina A.
This paper presents the results of a research project based on the Understandings of Consequence Project. This study motivated students to engage in inquiry in science classrooms. The complexity of the models is divided into four categories--underlying causality, relational causality, probabilistic causality, and emergent causality--and provides…
Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K.; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C.; Hoeng, Julia
2015-01-01
With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com PMID:25887162
Dynamical Causal Modeling from a Quantum Dynamical Perspective
NASA Astrophysics Data System (ADS)
Demiralp, Emre; Demiralp, Metin
2010-09-01
Recent research suggests that any set of first order linear vector ODEs can be converted to a set of specific vector ODEs adhering to what we have called "Quantum Harmonical Form (QHF)". QHF has been developed using a virtual quantum multi harmonic oscillator system where mass and force constants are considered to be time variant and the Hamiltonian is defined as a conic structure over positions and momenta to conserve the Hermiticity. As described in previous works, the conversion to QHF requires the matrix coefficient of the first set of ODEs to be a normal matrix. In this paper, this limitation is circumvented using a space extension approach expanding the potential applicability of this method. Overall, conversion to QHF allows the investigation of a set of ODEs using mathematical tools available to the investigation of the physical concepts underlying quantum harmonic oscillators. The utility of QHF in the context of dynamical systems and dynamical causal modeling in behavioral and cognitive neuroscience is briefly discussed.
Dynamical Causal Modeling from a Quantum Dynamical Perspective
Demiralp, Emre; Demiralp, Metin
2010-09-30
Recent research suggests that any set of first order linear vector ODEs can be converted to a set of specific vector ODEs adhering to what we have called ''Quantum Harmonical Form (QHF)''. QHF has been developed using a virtual quantum multi harmonic oscillator system where mass and force constants are considered to be time variant and the Hamiltonian is defined as a conic structure over positions and momenta to conserve the Hermiticity. As described in previous works, the conversion to QHF requires the matrix coefficient of the first set of ODEs to be a normal matrix. In this paper, this limitation is circumvented using a space extension approach expanding the potential applicability of this method. Overall, conversion to QHF allows the investigation of a set of ODEs using mathematical tools available to the investigation of the physical concepts underlying quantum harmonic oscillators. The utility of QHF in the context of dynamical systems and dynamical causal modeling in behavioral and cognitive neuroscience is briefly discussed.
Measured, modeled, and causal conceptions of fitness
Abrams, Marshall
2012-01-01
This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804
Dark matter perturbations and viscosity: A causal approach
NASA Astrophysics Data System (ADS)
Acquaviva, Giovanni; John, Anslyn; Pénin, Aurélie
2016-08-01
The inclusion of dissipative effects in cosmic fluids modifies their clustering properties and could have observable effects on the formation of large-scale structures. We analyze the evolution of density perturbations of cold dark matter endowed with causal bulk viscosity. The perturbative analysis is carried out in the Newtonian approximation and the bulk viscosity is described by the causal Israel-Stewart (IS) theory. In contrast to the noncausal Eckart theory, we obtain a third-order evolution equation for the density contrast that depends on three free parameters. For certain parameter values, the density contrast and growth factor in IS mimic their behavior in Λ CDM when z ≥1 . Interestingly, and contrary to intuition, certain sets of parameters lead to an increase of the clustering.
Multivariate dynamical systems models for estimating causal interactions in fMRI
Ryali, Srikanth; Supekar, Kaustubh; Chen, Tianwen; Menon, Vinod
2010-01-01
Analysis of dynamical interactions between distributed brain areas is of fundamental importance for understanding cognitive information processing. However, estimating dynamic causal interactions between brain regions using functional magnetic resonance imaging (fMRI) poses several unique challenges. For one, fMRI measures Blood Oxygenation Level Dependent (BOLD) signals, rather than the underlying latent neuronal activity. Second, regional variations in the hemodynamic response function (HRF) can significantly influence estimation of casual interactions between them. Third, causal interactions between brain regions can change with experimental context over time. To overcome these problems, we developed a novel state-space Multivariate Dynamical Systems (MDS) model to estimate intrinsic and experimentally-induced modulatory causal interactions between multiple brain regions. A probabilistic graphical framework is then used to estimate the parameters of MDS as applied to fMRI data. We show that MDS accurately takes into account regional variations in the HRF and estimates dynamic causal interactions at the level of latent signals. We develop and compare two estimation procedures using maximum likelihood estimation (MLE) and variational Bayesian (VB) approaches for inferring model parameters. Using extensive computer simulations, we demonstrate that, compared to Granger causal analysis (GCA), MDS exhibits superior performance for a wide range of signal to noise ratios (SNRs), sample length and network size. Our simulations also suggest that GCA fails to uncover causal interactions when there is a conflict between the direction of intrinsic and modulatory influences. Furthermore, we show that MDS estimation using VB methods is more robust and performs significantly better at low SNRs and shorter time series than MDS with MLE. Our study suggests that VB estimation of MDS provides a robust method for estimating and interpreting causal network interactions in fMRI data
Scale invariance in the causal approach to renormalization theory
NASA Astrophysics Data System (ADS)
Grigore, Dan R.
2001-06-01
The dilation invariance is studied in the framework of Epstein-Glaser approach to renormalization theory. Some analogues of the Callan-Symanzik equations are found and they are applied to the scalar field theory and to Yang-Mills models. We find the interesting result that, if all fields of the theory have zero masses, then from purely cohomological consideration, one can obtain the anomalous terms of logarithmic type.
There aren't plenty more fish in the sea: a causal network approach.
Nikolic, Milena; Lagnado, David A
2015-11-01
The current research investigated how lay representations of the causes of an environmental problem may underlie individuals' reasoning about the issue. Naïve participants completed an experiment that involved two main tasks. The causal diagram task required participants to depict the causal relations between a set of factors related to overfishing and to estimate the strength of these relations. The counterfactual task required participants to judge the effect of counterfactual suppositions based on the diagrammed factors. We explored two major questions: (1) what is the relation between individual causal models and counterfactual judgments? Consistent with previous findings (e.g., Green et al., 1998, Br. J. Soc. Psychology, 37, 415), these judgments were best explained by a combination of the strength of both direct and indirect causal paths. (2) To what extent do people use two-way causal thinking when reasoning about an environmental problem? In contrast to previous research (e.g., White, 2008, Appl. Cogn. Psychology, 22, 559), analyses based on individual causal networks revealed the presence of numerous feedback loops. The studies support the value of analysing individual causal models in contrast to consensual representations. Theoretical and practical implications are discussed in relation to causal reasoning as well as environmental psychology. PMID:25597224
Foliations and 2+1 causal dynamical triangulation models
Konopka, Tomasz
2006-01-15
The original models of causal dynamical triangulations construct space-time by arranging a set of simplices in layers separated by a fixed timelike distance. The importance of the foliation structure in the 2+1 dimensional model is studied by considering variations in which this property is relaxed. It turns out that the fixed-lapse condition can be equivalently replaced by a set of global constraints that have geometrical interpretation. On the other hand, the introduction of new types of simplices that puncture the foliating sheets in general leads to different low-energy behavior compared to the original model.
Causal models of trip replanning in TravTek
Schryver, J.C.
1998-07-01
The TravTek operational field test was conducted to evaluate the effectiveness of route planning, route guidance and various navigational aiding modalities for Advanced Traveler Information Systems in ground vehicles. A causal network was constructed in order to achieve a better understanding of the dependencies among variables implicated in the replanning process. Causal inferences were modeled using path analysis techniques. The original Yoked Driver study reported that addition of real-time navigation planning did not increase trip efficiency during initial trip planning. Data mining of the relatively complete database revealed that the incidence of dynamic trip replanning was only 0.51% or 1 out of every 198 trips. Nevertheless, the replanning acceptance rate was 92.8%, suggesting that less conservative criteria might have been acceptable to drivers. Several points can be made based upon the path analysis techniques. Drivers who rejected better route offers were more likely to be male renters; rejected routes were apparently offered at earlier times with a lower predicted time savings and fewer maneuvers. Failure to accept a better route also apparently resulted in fewer wrong-turn deviations. Contrary to expectations, wrong-turn count and time loss appeared as semi-independent hubs in the resultant causal network. Implications of the path analysis are discussed. Proposals for in-vehicle information systems are formulated to increase driver participation as co-planner, and increase the likelihood that trip replanning will positively impact trip efficiency.
Development of a causal model for elder mistreatment.
Pickering, Carolyn E Ziminski; Phillips, Linda R
2014-01-01
Elder mistreatment (EM) is an act committed by a person in a trusted relationship with an elderly person. Through the process of theory synthesis, a new model was developed, which explains the development of aggression (physical and verbal) toward elders by adult children in EM. The proposed model is set within the context of intimate partner violence and emphasizes that rather than arising in caregiving, aggression may be evident in the pre-caregiving relationship and continue into caregiving situations. An understanding of the causal origins of EM is essential in designing intervention to help families have healthy relationships. PMID:24547693
An Isometric Dynamics for a Causal Set Approach to Discrete Quantum Gravity
NASA Astrophysics Data System (ADS)
Gudder, S.
2015-12-01
We consider a covariant causal set approach to discrete quantum gravity. We first review the microscopic picture of this approach. In this picture a universe grows one element at a time and its geometry is determined by a sequence of integers called the shell sequence. We next present the macroscopic picture which is described by a sequential growth process. We introduce a model in which the dynamics is governed by a quantum transition amplitude. The amplitude satisfies a stochastic and unitary condition and the resulting dynamics becomes isometric. We show that the dynamics preserves stochastic states. By "doubling down" on the dynamics we obtain a unitary group representation and a natural energy operator. These unitary operators are employed to define canonical position and momentum operators.
Tractography-based priors for dynamic causal models
Stephan, Klaas Enno; Tittgemeyer, Marc; Knösche, Thomas R.; Moran, Rosalyn J.; Friston, Karl J.
2009-01-01
Functional integration in the brain rests on anatomical connectivity (the presence of axonal connections) and effective connectivity (the causal influences mediated by these connections). The deployment of anatomical connections provides important constraints on effective connectivity, but does not fully determine it, because synaptic connections can be expressed functionally in a dynamic and context-dependent fashion. Although it is generally assumed that anatomical connectivity data is important to guide the construction of neurobiologically realistic models of effective connectivity; the degree to which these models actually profit from anatomical constraints has not yet been formally investigated. Here, we use diffusion weighted imaging and probabilistic tractography to specify anatomically informed priors for dynamic causal models (DCMs) of fMRI data. We constructed 64 alternative DCMs, which embodied different mappings between the probability of an anatomical connection and the prior variance of the corresponding of effective connectivity, and fitted them to empirical fMRI data from 12 healthy subjects. Using Bayesian model selection, we show that the best model is one in which anatomical probability increases the prior variance of effective connectivity parameters in a nonlinear and monotonic (sigmoidal) fashion. This means that the higher the likelihood that a given connection exists anatomically, the larger one should set the prior variance of the corresponding coupling parameter; hence making it easier for the parameter to deviate from zero and represent a strong effective connection. To our knowledge, this study provides the first formal evidence that probabilistic knowledge of anatomical connectivity can improve models of functional integration. PMID:19523523
Causal Analysis After Haavelmo
Heckman, James; Pinto, Rodrigo
2014-01-01
Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123
Risk-Based Causal Modeling of Airborne Loss of Separation
NASA Technical Reports Server (NTRS)
Geuther, Steven C.; Shih, Ann T.
2015-01-01
Maintaining safe separation between aircraft remains one of the key aviation challenges as the Next Generation Air Transportation System (NextGen) emerges. The goals of the NextGen are to increase capacity and reduce flight delays to meet the aviation demand growth through the 2025 time frame while maintaining safety and efficiency. The envisioned NextGen is expected to enable high air traffic density, diverse fleet operations in the airspace, and a decrease in separation distance. All of these factors contribute to the potential for Loss of Separation (LOS) between aircraft. LOS is a precursor to a potential mid-air collision (MAC). The NASA Airspace Operations and Safety Program (AOSP) is committed to developing aircraft separation assurance concepts and technologies to mitigate LOS instances, therefore, preventing MAC. This paper focuses on the analysis of causal and contributing factors of LOS accidents and incidents leading to MAC occurrences. Mid-air collisions among large commercial aircraft are rare in the past decade, therefore, the LOS instances in this study are for general aviation using visual flight rules in the years 2000-2010. The study includes the investigation of causal paths leading to LOS, and the development of the Airborne Loss of Separation Analysis Model (ALOSAM) using Bayesian Belief Networks (BBN) to capture the multi-dependent relations of causal factors. The ALOSAM is currently a qualitative model, although further development could lead to a quantitative model. ALOSAM could then be used to perform impact analysis of concepts and technologies in the AOSP portfolio on the reduction of LOS risk.
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory
Gopnik, Alison; Wellman, Henry M.
2012-01-01
We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.
Gopnik, Alison; Wellman, Henry M
2012-11-01
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739
ERIC Educational Resources Information Center
Luque, David; Cobos, Pedro L.; Lopez, Francisco J.
2008-01-01
In an interference-between-cues design (IbC), the expression of a learned Cue A-Outcome 1 association has been shown to be impaired if another cue, B, is separately paired with the same outcome in a second learning phase. The present study examined whether IbC could be caused by associative mechanisms independent of causal reasoning processes.…
Spatiotemporal causal modeling for the management of Dengue Fever
NASA Astrophysics Data System (ADS)
Yu, Hwa-Lung; Huang, Tailin; Lee, Chieh-Han
2015-04-01
Increasing climatic extremes have caused growing concerns about the health effects and disease outbreaks. The association between climate variation and the occurrence of epidemic diseases play an important role on a country's public health systems. Part of the impacts are direct casualties associated with the increasing frequency and intensity of typhoons, the proliferation of disease vectors and the short-term increase of clinic visits on gastro-intestinal discomforts, diarrhea, dermatosis, or psychological trauma. Other impacts come indirectly from the influence of disasters on the ecological and socio-economic systems, including the changes of air/water quality, living environment and employment condition. Previous risk assessment studies on dengue fever focus mostly on climatic and non-climatic factors and their association with vectors' reproducing pattern. The public-health implication may appear simple. Considering the seasonal changes and regional differences, however, the causality of the impacts is full of uncertainties. Without further investigation, the underlying dengue fever risk dynamics may not be assessed accurately. The objective of this study is to develop an epistemic framework for assessing dynamic dengue fever risk across space and time. The proposed framework integrates cross-departmental data, including public-health databases, precipitation data over time and various socio-economic data. We explore public-health issues induced by typhoon through literature review and spatiotemporal analytic techniques on public health databases. From those data, we identify relevant variables and possible causal relationships, and their spatiotemporal patterns derived from our proposed spatiotemporal techniques. Eventually, we create a spatiotemporal causal network and a framework for modeling dynamic dengue fever risk.
On inference of causality for discrete state models in a multiscale context
Gerber, Susanne; Horenko, Illia
2014-01-01
Discrete state models are a common tool of modeling in many areas. E.g., Markov state models as a particular representative of this model family became one of the major instruments for analysis and understanding of processes in molecular dynamics (MD). Here we extend the scope of discrete state models to the case of systematically missing scales, resulting in a nonstationary and nonhomogeneous formulation of the inference problem. We demonstrate how the recently developed tools of nonstationary data analysis and information theory can be used to identify the simultaneously most optimal (in terms of describing the given data) and most simple (in terms of complexity and causality) discrete state models. We apply the resulting formalism to a problem from molecular dynamics and show how the results can be used to understand the spatial and temporal causality information beyond the usual assumptions. We demonstrate that the most optimal explanation for the appropriately discretized/coarse-grained MD torsion angles data in a polypeptide is given by the causality that is localized both in time and in space, opening new possibilities for deploying percolation theory and stochastic subgridscale modeling approaches in the area of MD. PMID:25267630
Zigler, Corwin Matthew; Dominici, Francesca
2014-01-01
Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of “big data” are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model in order to satisfy the assumptions necessary for estimating average causal effects. Typically, simple or ad-hoc methods are employed to arrive at a single PS model, without acknowledging the uncertainty associated with the model selection. We propose three Bayesian methods for PS variable selection and model averaging that 1) select relevant variables from a set of candidate variables to include in the PS model and 2) estimate causal treatment effects as weighted averages of estimates under different PS models. The associated weight for each PS model reflects the data-driven support for that model’s ability to adjust for the necessary variables. We illustrate features of our proposed approaches with a simulation study, and ultimately use our methods to compare the effectiveness of surgical vs. nonsurgical treatment for brain tumors among 2,606 Medicare beneficiaries. Supplementary materials are available online. PMID:24696528
De Stavola, Bianca L.; Daniel, Rhian M.; Ploubidis, George B.; Micali, Nadia
2015-01-01
The study of mediation has a long tradition in the social sciences and a relatively more recent one in epidemiology. The first school is linked to path analysis and structural equation models (SEMs), while the second is related mostly to methods developed within the potential outcomes approach to causal inference. By giving model-free definitions of direct and indirect effects and clear assumptions for their identification, the latter school has formalized notions intuitively developed in the former and has greatly increased the flexibility of the models involved. However, through its predominant focus on nonparametric identification, the causal inference approach to effect decomposition via natural effects is limited to settings that exclude intermediate confounders. Such confounders are naturally dealt with (albeit with the caveats of informality and modeling inflexibility) in the SEM framework. Therefore, it seems pertinent to revisit SEMs with intermediate confounders, armed with the formal definitions and (parametric) identification assumptions from causal inference. Here we investigate: 1) how identification assumptions affect the specification of SEMs, 2) whether the more restrictive SEM assumptions can be relaxed, and 3) whether existing sensitivity analyses can be extended to this setting. Data from the Avon Longitudinal Study of Parents and Children (1990–2005) are used for illustration. PMID:25504026
Optimal causal inference: Estimating stored information and approximating causal architecture
NASA Astrophysics Data System (ADS)
Still, Susanne; Crutchfield, James P.; Ellison, Christopher J.
2010-09-01
We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding—a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.
Causal Inference and Model Selection in Complex Settings
NASA Astrophysics Data System (ADS)
Zhao, Shandong
Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. In this article, we firstly review three main methods that generalize propensity scores in this direction, namely, inverse propensity weighting (IPW), the propensity function (P-FUNCTION), and the generalized propensity score (GPS), along with recent extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. We propose three new methods that provide robust causal estimation based on the P-FUNCTION and GPS. While our proposed P-FUNCTION-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation. In a related line of research, we consider adjustment for posttreatment covariates in causal inference. Even in a randomized experiment, observations might have different compliance performance under treatment and control assignment. This posttreatment covariate cannot be adjusted using standard statistical methods. We review the principal stratification framework which allows for modeling this effect as part of its Bayesian hierarchical models. We generalize the current model to add the possibility of adjusting for pretreatment covariates. We also propose a new estimator of the average treatment effect over the entire population. In a third line of research, we discuss the spectral line detection problem in high energy astrophysics. We carefully review how this problem can be statistically formulated as a precise hypothesis test with point null hypothesis, why a usual likelihood ratio test does not apply for problem of this nature, and a doable fix to correctly
Grief among Surviving Family Members of Homicide Victims: A Causal Approach.
ERIC Educational Resources Information Center
Sprang, M. Virginia; And Others
1993-01-01
Proposed causal model to delineate predictors of self-reported grief among surviving family members of homicide victims. Evaluated model using data from survey of members of "Victims of Violence" support groups. Results generally supported model and indicated that correlates of grief differed across gender-specific subgroups in terms of their…
Estimating causal effects for multivalued treatments: a comparison of approaches.
Linden, Ariel; Uysal, S Derya; Ryan, Andrew; Adams, John L
2016-02-20
Interventions with multivalued treatments are common in medical and health research, such as when comparing the efficacy of competing drugs or interventions, or comparing between various doses of a particular drug. In recent years, there has been a growing interest in the development of multivalued treatment effect estimators using observational data. In this paper, we compare the performance of commonly used regression-based methods that estimate multivalued treatment effects based on the unconfoundedness assumption. These estimation methods fall into three general categories: (i) estimators based on a model for the outcome variable using conventional regression adjustment; (ii) weighted estimators based on a model for the treatment assignment; and (iii) 'doubly-robust' estimators that model both the treatment assignment and outcome variable within the same framework. We assess the performance of these models using Monte Carlo simulation and demonstrate their application with empirical data. Our results show that (i) when models estimating both the treatment and outcome are correctly specified, all adjustment methods provide similar unbiased estimates; (ii) when the outcome model is misspecified, regression adjustment performs poorly, while all the weighting methods provide unbiased estimates; (iii) when the treatment model is misspecified, methods based solely on modeling the treatment perform poorly, while regression adjustment and the doubly robust models provide unbiased estimates; and (iv) when both the treatment and outcome models are misspecified, all methods perform poorly. Given that researchers will rarely know which of the two models is misspecified, our results support the use of doubly robust estimation. PMID:26482211
Reininghaus, Ulrich; Depp, Colin A; Myin-Germeys, Inez
2016-03-01
Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals' daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. PMID:26707864
NASA Astrophysics Data System (ADS)
Besson, Ugo
2010-03-01
This paper presents an analysis of the different types of reasoning and physical explanation used in science, common thought, and physics teaching. It then reflects on the learning difficulties connected with these various approaches, and suggests some possible didactic strategies. Although causal reasoning occurs very frequently in common thought and daily life, it has long been the subject of debate and criticism among philosophers and scientists. In this paper, I begin by providing a description of some general tendencies of common reasoning that have been identified by didactic research. Thereafter, I briefly discuss the role of causality in science, as well as some different types of explanation employed in the field of physics. I then present some results of a study examining the causal reasoning used by students in solid and fluid mechanics. The differences found between the types of reasoning typical of common thought and those usually proposed during instruction can create learning difficulties and impede student motivation. Many students do not seem satisfied by the mere application of formal laws and functional relations. Instead, they express the need for a causal explanation, a mechanism that allows them to understand how a state of affairs has come about. I discuss few didactic strategies aimed at overcoming these problems, and describe, in general terms, two examples of mechanics teaching sequences which were developed and tested in different contexts. The paper ends with a reflection on the possible role to be played in physics learning by intuitive and imaginative thought, and the use of simple explanatory models based on physical analogies and causal mechanisms.
Counterfactuals and Causal Models: Introduction to the Special Issue
ERIC Educational Resources Information Center
Sloman, Steven A.
2013-01-01
Judea Pearl won the 2010 Rumelhart Prize in computational cognitive science due to his seminal contributions to the development of Bayes nets and causal Bayes nets, frameworks that are central to multiple domains of the computational study of mind. At the heart of the causal Bayes nets formalism is the notion of a counterfactual, a representation…
Cause and Event: Supporting Causal Claims through Logistic Models
ERIC Educational Resources Information Center
O'Connell, Ann A.; Gray, DeLeon L.
2011-01-01
Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify…
Ryali, Srikanth; Ian Shih, Yen-Yu; Chen, Tianwen; Kochalka, John; Albaugh, Daniel; Fang, Zhongnan; Supekar, Kaustubh; Lee, Jin Hyung; Menon, Vinod
2016-01-01
State-space multivariate dynamical systems (MDS) (Ryali et al., 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods is poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More
Ryali, Srikanth; Shih, Yen-Yu Ian; Chen, Tianwen; Kochalka, John; Albaugh, Daniel; Fang, Zhongnan; Supekar, Kaustubh; Lee, Jin Hyung; Menon, Vinod
2016-05-15
State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort, optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in f
Duckworth, Angela Lee; Tsukayama, Eli; May, Henry
2010-01-01
The predictive validity of personality for important life outcomes is well established, but conventional longitudinal analyses cannot rule out the possibility that unmeasured third-variable confounds fully account for the observed relationships. Longitudinal hierarchical linear models (HLM) with time-varying covariates allow each subject to serve as his or her own control, thus eliminating between-individual confounds. HLM also allows the directionality of the causal relationship to be tested by reversing time-lagged predictor and outcome variables. We illustrate these techniques through a series of models that demonstrate that within-individual changes in self-control over time predict subsequent changes in GPA but not vice-versa. The evidence supporting a causal role for self-control was not moderated by IQ, gender, ethnicity, or income. Further analyses rule out one time-varying confound: self-esteem. The analytic approach taken in this study provides the strongest evidence to date for the causal role of self-control in determining achievement. PMID:20976121
Gradient-based MCMC samplers for dynamic causal modelling
Sengupta, Biswa; Friston, Karl J.; Penny, Will D.
2016-01-01
In this technical note, we derive two MCMC (Markov chain Monte Carlo) samplers for dynamic causal models (DCMs). Specifically, we use (a) Hamiltonian MCMC (HMC-E) where sampling is simulated using Hamilton’s equation of motion and (b) Langevin Monte Carlo algorithm (LMC-R and LMC-E) that simulates the Langevin diffusion of samples using gradients either on a Euclidean (E) or on a Riemannian (R) manifold. While LMC-R requires minimal tuning, the implementation of HMC-E is heavily dependent on its tuning parameters. These parameters are therefore optimised by learning a Gaussian process model of the time-normalised sample correlation matrix. This allows one to formulate an objective function that balances tuning parameter exploration and exploitation, furnishing an intervention-free inference scheme. Using neural mass models (NMMs)—a class of biophysically motivated DCMs—we find that HMC-E is statistically more efficient than LMC-R (with a Riemannian metric); yet both gradient-based samplers are far superior to the random walk Metropolis algorithm, which proves inadequate to steer away from dynamical instability. PMID:26213349
Dynamic causal modelling for functional near-infrared spectroscopy
Tak, S.; Kempny, A.M.; Friston, K.J.; Leff, A.P.; Penny, W.D.
2015-01-01
Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes. PMID:25724757
Supekar, Kaustubh; Menon, Vinod
2012-02-01
Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition. One skill that develops over time is the ability to problem solve, which in turn relies on cognitive control and attention abilities. Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI, resting-state fMRI and diffusion tensor imaging (DTI) to investigate the maturation of control processes underlying problem solving skills in 7-9 year-old children. Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network, anchored in anterior insula (AI), ventrolateral prefrontal cortex and anterior cingulate cortex, and the fronto-parietal central executive network, anchored in dorsolateral prefrontal cortex and posterior parietal cortex (PPC). We found that, by age 9, the AI node of the salience network is a major causal hub initiating control signals during problem solving. Critically, despite stronger AI activation, the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults. These results were validated using two different analytic methods for estimating causal interactions in fMRI data. In parallel, DTI-based tractography revealed weaker AI-PPC structural connectivity in children. Our findings point to a crucial role of AI connectivity, and its causal cross-network influences, in the maturation of dynamic top-down control signals underlying cognitive development. Overall, our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive development. The
ERIC Educational Resources Information Center
Jensen, Eva
2014-01-01
If students really understand the systems they study, they would be able to tell how changes in the system would affect a result. This demands that the students understand the mechanisms that drive its behaviour. The study investigates potential merits of learning how to explicitly model the causal structure of systems. The approach and…
Causal Modeling--Path Analysis a New Trend in Research in Applied Linguistics
ERIC Educational Resources Information Center
Rastegar, Mina
2006-01-01
This article aims at discussing a new statistical trend in research in applied linguistics. This rather new statistical procedure is causal modeling--path analysis. The article demonstrates that causal modeling--path analysis is the best statistical option to use when the effects of a multitude of L2 learners' variables on language achievement are…
Sensory Impairments and Autism: A Re-Examination of Causal Modelling
ERIC Educational Resources Information Center
Gerrard, Sue; Rugg, Gordon
2009-01-01
Sensory impairments are widely reported in autism, but remain largely unexplained by existing models. This article examines Kanner's causal reasoning and identifies unsupported assumptions implicit in later empirical work. Our analysis supports a heterogeneous causal model for autistic characteristics. We propose that the development of a…
The causal pie model: an epidemiological method applied to evolutionary biology and ecology.
Wensink, Maarten; Westendorp, Rudi G J; Baudisch, Annette
2014-05-01
A general concept for thinking about causality facilitates swift comprehension of results, and the vocabulary that belongs to the concept is instrumental in cross-disciplinary communication. The causal pie model has fulfilled this role in epidemiology and could be of similar value in evolutionary biology and ecology. In the causal pie model, outcomes result from sufficient causes. Each sufficient cause is made up of a "causal pie" of "component causes". Several different causal pies may exist for the same outcome. If and only if all component causes of a sufficient cause are present, that is, a causal pie is complete, does the outcome occur. The effect of a component cause hence depends on the presence of the other component causes that constitute some causal pie. Because all component causes are equally and fully causative for the outcome, the sum of causes for some outcome exceeds 100%. The causal pie model provides a way of thinking that maps into a number of recurrent themes in evolutionary biology and ecology: It charts when component causes have an effect and are subject to natural selection, and how component causes affect selection on other component causes; which partitions of outcomes with respect to causes are feasible and useful; and how to view the composition of a(n apparently homogeneous) population. The diversity of specific results that is directly understood from the causal pie model is a test for both the validity and the applicability of the model. The causal pie model provides a common language in which results across disciplines can be communicated and serves as a template along which future causal analyses can be made. PMID:24963386
Trajectories and causal phase-space approach to relativistic quantum mechanics
Holland, P.R.; Kyprianidis, A.; Vigier, J.P.
1987-05-01
The authors analyze phase-space approaches to relativistic quantum mechanics from the viewpoint of the causal interpretation. In particular, they discuss the canonical phase space associated with stochastic quantization, its relation to Hilbert space, and the Wigner-Moyal formalism. They then consider the nature of Feynman paths, and the problem of nonlocality, and conclude that a perfectly consistent relativistically covariant interpretation of quantum mechanics which retains the notion of particle trajectory is possible.
NASA Astrophysics Data System (ADS)
Poppe, Michaela; Zitek, Andreas; Salles, Paulo; Bredeweg, Bert; Muhar, Susanne
2010-05-01
The education system needs strategies to attract future scientists and practitioners. There is an alarming decline in the number of students choosing science subjects. Reasons for this include the perceived complexity and the lack of effective cognitive tools that enable learners to acquire the expertise in a way that fits its qualitative nature. The DynaLearn project utilises a "Learning by modelling" approach to deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge. The modelling approach is based on qualitative reasoning, a research area within artificial intelligence, and allows for capturing and simulating qualitative systems knowledge. Educational activities within the DynaLearn software address topics at different levels of complexity, depending on the educational goals and settings. DynaLearn uses virtual characters in the learning environment as agents for engaging and motivating the students during their modelling exercise. The DynaLearn software represents an interactive learning environment in which learners are in control of their learning activities. The software is able to coach them individually based on their current progress, their knowledge needs and learning goals. Within the project 70 expert models on different environmental issues covering seven core topics (Earth Systems and Resources, The Living World, Human population, Land and Water Use, Energy Resources and Consumption, Pollution, and Global Changes) will be delivered. In the context of the core topic "Land and Water Use" the Institute of Hydrobiology and Aquatic Ecosystem Management has developed a model on Sustainable River Catchment Management. River systems with their catchments have been tremendously altered due to human pressures with serious consequences for the ecological integrity of riverine landscapes. The operation of hydropower plants, the implementation of flood protection measures, the regulation of flow and sediment regime and intensive
Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference
ERIC Educational Resources Information Center
Schochet, Peter Z.
2013-01-01
This article examines the estimation of two-stage clustered designs for education randomized control trials (RCTs) using the nonparametric Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for…
Causal Agency Theory: Reconceptualizing a Functional Model of Self-Determination
ERIC Educational Resources Information Center
Shogren, Karrie A.; Wehmeyer, Michael L.; Palmer, Susan B.; Forber-Pratt, Anjali J.; Little, Todd J.; Lopez, Shane
2015-01-01
This paper introduces Causal Agency Theory, an extension of the functional model of self-determination. Causal Agency Theory addresses the need for interventions and assessments pertaining to selfdetermination for all students and incorporates the significant advances in understanding of disability and in the field of positive psychology since the…
Causality and headache triggers
Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.
2013-01-01
Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872
Dynamic causal models of neural system dynamics: current state and future extensions
Stephan, Klaas E.; Harrison, Lee M.; Kiebel, Stefan J.; David, Olivier; Penny, Will D.; Friston, Karl J.
2009-01-01
Complex processes resulting from the interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additionally, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, Dynamic Causal Modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian Model Selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity. PMID:17426386
Causal inference based on counterfactuals
Höfler, M
2005-01-01
Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept. PMID:16159397
Lin, Ting; Harmsen, Stephen C.; Baker, Jack W.; Luco, Nicolas
2013-01-01
The conditional spectrum (CS) is a target spectrum (with conditional mean and conditional standard deviation) that links seismic hazard information with ground-motion selection for nonlinear dynamic analysis. Probabilistic seismic hazard analysis (PSHA) estimates the ground-motion hazard by incorporating the aleatory uncertainties in all earthquake scenarios and resulting ground motions, as well as the epistemic uncertainties in ground-motion prediction models (GMPMs) and seismic source models. Typical CS calculations to date are produced for a single earthquake scenario using a single GMPM, but more precise use requires consideration of at least multiple causal earthquakes and multiple GMPMs that are often considered in a PSHA computation. This paper presents the mathematics underlying these more precise CS calculations. Despite requiring more effort to compute than approximate calculations using a single causal earthquake and GMPM, the proposed approach produces an exact output that has a theoretical basis. To demonstrate the results of this approach and compare the exact and approximate calculations, several example calculations are performed for real sites in the western United States. The results also provide some insights regarding the circumstances under which approximate results are likely to closely match more exact results. To facilitate these more precise calculations for real applications, the exact CS calculations can now be performed for real sites in the United States using new deaggregation features in the U.S. Geological Survey hazard mapping tools. Details regarding this implementation are discussed in this paper.
ERIC Educational Resources Information Center
Hayduk, Leslie
2014-01-01
Researchers using factor analysis tend to dismiss the significant ill fit of factor models by presuming that if their factor model is close-to-fitting, it is probably close to being properly causally specified. Close fit may indeed result from a model being close to properly causally specified, but close-fitting factor models can also be seriously…
Visual Causal Models Enhance Clinical Explanations of Treatments for Generalized Anxiety Disorder
Kim, Nancy S.; Khalife, Danielle; Judge, Kelly A.; Paulus, Daniel J.; Jordan, Jake T.; Yopchick, Jennelle E.
2013-01-01
A daily challenge in clinical practice is to adequately explain disorders and treatments to patients of varying levels of literacy in a time-limited situation. Drawing jointly upon research on causal reasoning and multimodal theory, the authors asked whether adding visual causal models to clinical explanations promotes patient learning. Participants were 86 people currently or formerly diagnosed with a mood disorder and 104 lay people in Boston, Massachusetts, USA, who were randomly assigned to receive either a visual causal model (dual-mode) presentation or auditory-only presentation of an explanation about generalized anxiety disorder and its treatment. Participants' knowledge was tested before, immediately after, and 4 weeks after the presentation. Patients and lay people learned significantly more from visual causal model presentations than from auditory-only presentations, and visual causal models were perceived to be helpful. Participants retained some information 4 weeks after the presentation, although the advantage of visual causal models did not persist in the long term. In conclusion, dual-mode presentations featuring visual causal models yield significant relative gains in patient comprehension immediately after the clinical session, at a time when the authors suggest that patients may be most willing to begin the recommended treatment plan. PMID:24093349
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
ERIC Educational Resources Information Center
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
Wolff, Phillip; Barbey, Aron K.
2015-01-01
Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611
Causal modelling applied to the risk assessment of a wastewater discharge.
Paul, Warren L; Rokahr, Pat A; Webb, Jeff M; Rees, Gavin N; Clune, Tim S
2016-03-01
Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information. This merger has clarified the causal content of SEMs and generalised the method such that it can now be performed using standard statistical techniques. As it was with BNs, the utility of this new generation of SEM in ecological risk assessment will need to be demonstrated with examples to foster an understanding and acceptance of the method. Here, we applied SEM to the risk assessment of a wastewater discharge to a stream, with a particular focus on the process of translating a causal diagram (conceptual model) into a statistical model which might then be used in the decision-making and evaluation stages of the risk assessment. The process of building and testing a spatial causal model is demonstrated using data from a spatial sampling design, and the implications of the resulting model are discussed in terms of the risk assessment. It is argued that a spatiotemporal causal model would have greater external validity than the spatial model, enabling broader generalisations to be made regarding the impact of a discharge, and greater value as a tool for evaluating the effects of potential treatment plant upgrades. Suggestions are made on how the causal model could be augmented to include temporal as well as spatial information, including suggestions for appropriate statistical models and analyses. PMID:26832914
NASA Astrophysics Data System (ADS)
Jensen, Eva
2014-07-01
If students really understand the systems they study, they would be able to tell how changes in the system would affect a result. This demands that the students understand the mechanisms that drive its behaviour. The study investigates potential merits of learning how to explicitly model the causal structure of systems. The approach and performance of 15 system dynamics students who are taught to explicitly model the causal structure of the systems they study were compared with the approach and performance of 22 engineering students, who generally did not receive such training. The task was to bring a computer-simulated predator-and-prey ecology to equilibrium. The system dynamics students were significantly more likely than the engineering students to correctly frame the problem. They were not much better at solving the task, however. It seemed that they had only learnt how to make models and not how to use them for reasoning.
Zheng, Wei; Wu, Chunxian
2015-01-01
Structural health monitoring (SHM) is challenged by massive data storage pressure and structural fault location. In response to these issues, a bio-inspired memory model that is embedded with a causality reasoning function is proposed for fault location. First, the SHM data for processing are divided into three temporal memory areas to control data volume reasonably. Second, the inherent potential of the causal relationships in structural state monitoring is mined. Causality and dependence indices are also proposed to establish the mechanism of quantitative description of the reason and result events. Third, a mechanism of causality reasoning is developed for the reason and result events to locate faults in a SHM system. Finally, a deformation experiment conducted on a steel spring plate demonstrates that the proposed model can be applied to real-time acquisition, compact data storage, and system fault location in a SHM system. Moreover, the model is compared with some typical methods based on an experimental benchmark dataset. PMID:25798991
Guarnera, Enrico; Berezovsky, Igor N.
2016-01-01
Allostery is one of the pervasive mechanisms through which proteins in living systems carry out enzymatic activity, cell signaling, and metabolism control. Effective modeling of the protein function regulation requires a synthesis of the thermodynamic and structural views of allostery. We present here a structure-based statistical mechanical model of allostery, allowing one to observe causality of communication between regulatory and functional sites, and to estimate per residue free energy changes. Based on the consideration of ligand free and ligand bound systems in the context of a harmonic model, corresponding sets of characteristic normal modes are obtained and used as inputs for an allosteric potential. This potential quantifies the mean work exerted on a residue due to the local motion of its neighbors. Subsequently, in a statistical mechanical framework the entropic contribution to allosteric free energy of a residue is directly calculated from the comparison of conformational ensembles in the ligand free and ligand bound systems. As a result, this method provides a systematic approach for analyzing the energetics of allosteric communication based on a single structure. The feasibility of the approach was tested on a variety of allosteric proteins, heterogeneous in terms of size, topology and degree of oligomerization. The allosteric free energy calculations show the diversity of ways and complexity of scenarios existing in the phenomenology of allosteric causality and communication. The presented model is a step forward in developing the computational techniques aimed at detecting allosteric sites and obtaining the discriminative power between agonistic and antagonistic effectors, which are among the major goals in allosteric drug design. PMID:26939022
Guarnera, Enrico; Berezovsky, Igor N
2016-03-01
Allostery is one of the pervasive mechanisms through which proteins in living systems carry out enzymatic activity, cell signaling, and metabolism control. Effective modeling of the protein function regulation requires a synthesis of the thermodynamic and structural views of allostery. We present here a structure-based statistical mechanical model of allostery, allowing one to observe causality of communication between regulatory and functional sites, and to estimate per residue free energy changes. Based on the consideration of ligand free and ligand bound systems in the context of a harmonic model, corresponding sets of characteristic normal modes are obtained and used as inputs for an allosteric potential. This potential quantifies the mean work exerted on a residue due to the local motion of its neighbors. Subsequently, in a statistical mechanical framework the entropic contribution to allosteric free energy of a residue is directly calculated from the comparison of conformational ensembles in the ligand free and ligand bound systems. As a result, this method provides a systematic approach for analyzing the energetics of allosteric communication based on a single structure. The feasibility of the approach was tested on a variety of allosteric proteins, heterogeneous in terms of size, topology and degree of oligomerization. The allosteric free energy calculations show the diversity of ways and complexity of scenarios existing in the phenomenology of allosteric causality and communication. The presented model is a step forward in developing the computational techniques aimed at detecting allosteric sites and obtaining the discriminative power between agonistic and antagonistic effectors, which are among the major goals in allosteric drug design. PMID:26939022
A new life-span approach to conscientiousness and health: combining the pieces of the causal puzzle.
Friedman, Howard S; Kern, Margaret L; Hampson, Sarah E; Duckworth, Angela Lee
2014-05-01
Conscientiousness has been shown to predict healthy behaviors, healthy social relationships, and physical health and longevity. The causal links, however, are complex and not well elaborated. Many extant studies have used comparable measures for conscientiousness, and a systematic endeavor to build cross-study analyses for conscientiousness and health now seems feasible. Of particular interest are efforts to construct new, more comprehensive causal models by linking findings and combining data from existing studies of different cohorts. Although methodological perils can threaten such integration, such efforts offer an early opportunity to enliven a life course perspective on conscientiousness, to see whether component facets of conscientiousness remain related to each other and to relevant mediators across broad spans of time, and to bolster the findings of the few long-term longitudinal studies of the dynamics of personality and health. A promising approach to testing new models involves pooling data from extant studies as an efficient and heuristic prelude to large-scale testing of interventions. PMID:23088747
Michalareas, George; Schoffelen, Jan-Mathijs; Paterson, Gavin; Gross, Joachim
2013-01-01
Abstract In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22328419
A restricted dimer model on a two-dimensional random causal triangulation
NASA Astrophysics Data System (ADS)
Ambjørn, J.; Durhuus, B.; Wheater, J. F.
2014-09-01
We introduce a restricted hard dimer model on a random causal triangulation that is exactly solvable and generalizes a model recently proposed by Atkin and Zohren (2012 Phys. Lett. B 712 445-50). We show that the latter model exhibits unusual behaviour at its multicritical point; in particular, its Hausdorff dimension equals 3 and not 3/2 as would be expected from general scaling arguments. When viewed as a special case of the generalized model introduced here we show that this behaviour is not generic and therefore is not likely to represent the true behaviour of the full dimer model on a random causal triangulation.
Expectations and Interpretations During Causal Learning
Luhmann, Christian C.; Ahn, Woo-kyoung
2012-01-01
In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants’ interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. PMID:21534705
Making the Case for Causal Dynamical Triangulations
NASA Astrophysics Data System (ADS)
Cooperman, Joshua H.
2015-11-01
The aim of the causal dynamical triangulations approach is to define nonperturbatively a quantum theory of gravity as the continuum limit of a lattice-regularized model of dynamical geometry. My aim in this paper is to give a concise yet comprehensive, impartial yet personal presentation of the causal dynamical triangulations approach.
Campbell's and Rubin's Perspectives on Causal Inference
ERIC Educational Resources Information Center
West, Stephen G.; Thoemmes, Felix
2010-01-01
Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…
Introduction to Causal Dynamical Triangulations
NASA Astrophysics Data System (ADS)
Görlich, Andrzej
The method of causal dynamical triangulations is a non-perturbative and background-independent approach to quantum theory of gravity. In this review we present recent results obtained within the four dimensional model of causal dynamical triangulations. We describe the phase structure of the model and demonstrate how a macroscopic four-dimensional de Sitter universe emerges dynamically from the full gravitational path integral. We show how to reconstruct the effective action describing scale factor fluctuations from Monte Carlo data.
Promoting Causal Agency: The Self-Determination Learning Model of Instruction.
ERIC Educational Resources Information Center
Wehmeyer, Michael L.; Palmer, Susan B.; Agran, Martin; Mithaug, Dennis E.; Martin, James E.
2000-01-01
This article introduces a model of teaching, The Self-Determined Learning Model of Instruction, incorporating principles of self-determination, which enables teachers to teach students to become causal agents in their own lives. Results from a field test indicate students (N=40) receiving instruction from teachers using the model attained…
A Tool To Support Failure Mode And Effects Analysis Based On Causal Modelling And Reasoning
NASA Astrophysics Data System (ADS)
Underwood, W. E.; Laib, S. L.
1987-05-01
A prototype knowledge-based system has been developed that supports Failure Mode & Effects Analysis (FMEA). The knowledge base consists of causal models of components and a representation for coupling these components into assemblies and systems. The causal models are qualitative models. They allow reasoning as to whether variables are increasing, decreasing or steady. The analysis strategies used by the prototype allow it to determine the effects of failure modes on the function of the part, the failure effect on the assembly the part is contained in, and the effect on the subsystem containing the assembly.
Morabia, Alfredo
2005-01-01
Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population. PMID:16898206
Scheduling with partial orders and a causal model
NASA Technical Reports Server (NTRS)
Boddy, Mark; Carciofini, Jim; Hadden, George D.
1993-01-01
In an ongoing project at Honeywell SRC, we are constructing a prototype scheduling system for a NASA domain using the 'Time Map Manager' (TMM). The TMM representations are flexible enough to permit the representation of precedence constraints, metric constraints between activities, and constraints relative to a variety of references (e.g., Mission Elapsed Time vs. Mission Day). The TMM also supports a simple form of causal reasoning (projection), dynamic database updates, and monitoring specified database properties as changes occur over time. The greatest apparent advantage to using the TMM is the flexibility added to the scheduling process: schedules are constructed by a process of 'iterative refinement,' in which scheduling decisions correspond to constraining an activity either with respect to another activity or with respect to one time line. The schedule becomes more detailed as activities and constraints are added. Undoing a scheduling decision means removing a constraint, not removing an activity from a specified place on the time line. For example, we can move an activity around on the time line by deleting constraints and adding new ones.
Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI.
Koush, Yury; Rosa, Maria Joao; Robineau, Fabien; Heinen, Klaartje; W Rieger, Sebastian; Weiskopf, Nikolaus; Vuilleumier, Patrik; Van De Ville, Dimitri; Scharnowski, Frank
2013-11-01
Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual-spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks. PMID:23668967
ERIC Educational Resources Information Center
Besson, Ugo
2010-01-01
This paper presents an analysis of the different types of reasoning and physical explanation used in science, common thought, and physics teaching. It then reflects on the learning difficulties connected with these various approaches, and suggests some possible didactic strategies. Although causal reasoning occurs very frequently in common thought…
Billoud, Bernard; Jouanno, Émilie; Nehr, Zofia; Carton, Baptiste; Rolland, Élodie; Chenivesse, Sabine; Charrier, Bénédicte
2015-01-01
Mutagenesis is the only process by which unpredicted biological gene function can be identified. Despite that several macroalgal developmental mutants have been generated, their causal mutation was never identified, because experimental conditions were not gathered at that time. Today, progresses in macroalgal genomics and judicious choices of suitable genetic models make mutated gene identification possible. This article presents a comparative study of two methods aiming at identifying a genetic locus in the brown alga Ectocarpus siliculosus: positional cloning and Next-Generation Sequencing (NGS)-based mapping. Once necessary preliminary experimental tools were gathered, we tested both analyses on an Ectocarpus morphogenetic mutant. We show how a narrower localization results from the combination of the two methods. Advantages and drawbacks of these two approaches as well as potential transfer to other macroalgae are discussed. PMID:25745426
Billoud, Bernard; Jouanno, Émilie; Nehr, Zofia; Carton, Baptiste; Rolland, Élodie; Chenivesse, Sabine; Charrier, Bénédicte
2015-01-01
Mutagenesis is the only process by which unpredicted biological gene function can be identified. Despite that several macroalgal developmental mutants have been generated, their causal mutation was never identified, because experimental conditions were not gathered at that time. Today, progresses in macroalgal genomics and judicious choices of suitable genetic models make mutated gene identification possible. This article presents a comparative study of two methods aiming at identifying a genetic locus in the brown alga Ectocarpus siliculosus: positional cloning and Next-Generation Sequencing (NGS)-based mapping. Once necessary preliminary experimental tools were gathered, we tested both analyses on an Ectocarpus morphogenetic mutant. We show how a narrower localization results from the combination of the two methods. Advantages and drawbacks of these two approaches as well as potential transfer to other macroalgae are discussed. PMID:25745426
A Typology of Causal Models for Plate Tectonics: Inferential Power and Barriers to Understanding.
ERIC Educational Resources Information Center
Gobert, Janice D.
2000-01-01
Analyzes fifth grade students' diagrams and explanations for the spatial, causal, and dynamic processes inside the earth. Identifies and characterizes the different types of student models for the inside of the earth, and characterizes the reasoning associated with these models. (Contains 74 references.) (Author/YDS)
Sex and Self-Control Theory: The Measures and Causal Model May Be Different
ERIC Educational Resources Information Center
Higgins, George E.; Tewksbury, Richard
2006-01-01
This study examines the distribution differences across sexes in key measures of self-control theory and differences in a causal model. Using cross-sectional data from juveniles ("n" = 1,500), the study shows mean-level differences in many of the self-control, risky behavior, and delinquency measures. Structural equation modeling findings support…
Ness, Robert O; Sachs, Karen; Vitek, Olga
2016-03-01
Causal inference, the task of uncovering regulatory relationships between components of biomolecular pathways and networks, is a primary goal of many high-throughput investigations. Statistical associations between observed protein concentrations can suggest an enticing number of hypotheses regarding the underlying causal interactions, but when do such associations reflect the underlying causal biomolecular mechanisms? The goal of this perspective is to provide suggestions for causal inference in large-scale experiments, which utilize high-throughput technologies such as mass-spectrometry-based proteomics. We describe in nontechnical terms the pitfalls of inference in large data sets and suggest methods to overcome these pitfalls and reliably find regulatory associations. PMID:26731284
Causality-weighted active learning for abnormal event identification based on the topic model
NASA Astrophysics Data System (ADS)
Fan, Yawen; Zheng, Shibao; Yang, Hua; Zhang, Chongyang; Su, Hang
2012-07-01
Abnormal event identification in crowded scenes is a fundamental task for video surveillance. However, it is still challenging for most current approaches because of the general insufficiency of labeled data for training, particularly for abnormal data. We propose a novel active-supervised joint topic model for learning activity and training sample collection. First, a multi-class topic model is constructed based on the initial training data. Then the remaining unlabeled data stream is surveyed. The system actively decides whether it can label a new sample by itself or if it has to ask a human annotator. After each query, the current model is incrementally updated. To alleviate class imbalance, causality-weighted method is applied to both likelihood and uncertainty sampling for active learning. Furthermore, a combination of a new measure termed query entropy and the overall classification accuracy is used for assessing the model performance. Experimental results on two real-world traffic videos for abnormal event identification tasks demonstrate the effectiveness of the proposed method.
Synchronizaton and causality across time-scales of observed and modelled ENSO dynamics
NASA Astrophysics Data System (ADS)
Jajcay, Nikola; Kravtsov, Sergey; Tsonis, Anastasios A.; Paluš, Milan
2016-04-01
Phase-phase and phase-amplitude interactions between dynamics on different temporal scales has been observed in ENSO dynamics, captured by the NINO3.4 index, using the approach for identification of cross-scale interactions introduced recently by Paluš [1]. The most pronounced interactions across scales are phase coherence and phase-phase causality in which the annual cycle influences the dynamics on the quasibiennial scale. The phase of slower phenomena on the scale 4-6 years influences not only the combination frequencies around the period one year, but also the phase of the annual cycle and also the amplitude of the oscillations in the quasibiennial range. In order to understand these nonlinear phenomena we investigate cross-scale interactions in synthetic, modelled NINO3.4 time series. The models taken into account were a selection of 96 historic runs from CMIP5 project, and two low-dimensional models - parametric recharge oscillator (PRO) [2], which is a two-dimensional dynamical model and a data-driven model based on the idea of linear inverse models [3]. The latter is a statistical model, in our setting 25-dimensional. While the two dimensions of the PRO model are not enough to capture all the cross-scale interactions, the results from the data-driven model are more promising and they resemble the interactions found in NINO3.4 measured data set. We believe that combination of models of different complexity will help to uncover mechanisms of the cross-scale interactions which might be the key for better understanding of the irregularities in the ENSO dynamics. This study is supported by the Ministry of Education, Youth and Sports of the Czech Republic within the Program KONTAKT II, Project No. LH14001. [1] M. Palus, Phys. Rev. Let. 112 078702 (2014) [2] K. Stein et al., J. Climate, 27, 14 (2014) [3] Kondrashov et al., J. Climate, 18, 21 (2005)
Suárez-Vega, Aroa; Gutiérrez-Gil, Beatriz; Benavides, Julio; Perez, Valentín; Tosser-Klopp, Gwenola; Klopp, Christophe; Keennel, Stephen J; Arranz, Juan José
2015-01-01
In this study, we demonstrate the use of a genome-wide association mapping together with RNA-seq in a reduced number of samples, as an efficient approach to detect the causal mutation for a Mendelian disease. Junctional epidermolysis bullosa is a recessive genodermatosis that manifests with neonatal mechanical fragility of the skin, blistering confined to the lamina lucida of the basement membrane and severe alteration of the hemidesmosomal junctions. In Spanish Churra sheep, junctional epidermolysis bullosa (JEB) has been detected in two commercial flocks. The JEB locus was mapped to Ovis aries chromosome 11 by GWAS and subsequently fine-mapped to an 868-kb homozygous segment using the identical-by-descent method. The ITGB4, which is located within this region, was identified as the best positional and functional candidate gene. The RNA-seq variant analysis enabled us to discover a 4-bp deletion within exon 33 of the ITGB4 gene (c.4412_4415del). The c.4412_4415del mutation causes a frameshift resulting in a premature stop codon at position 1472 of the integrin β4 protein. A functional analysis of this deletion revealed decreased levels of mRNA in JEB skin samples and the absence of integrin β4 labeling in immunohistochemical assays. Genotyping of c.4412_4415del showed perfect concordance with the recessive mode of the disease phenotype. Selection against this causal mutation will now be used to solve the problem of JEB in flocks of Churra sheep. Furthermore, the identification of the ITGB4 mutation means that affected sheep can be used as a large mammal animal model for the human form of epidermolysis bullosa with aplasia cutis. Our approach evidences that RNA-seq offers cost-effective alternative to identify variants in the species in which high resolution exome-sequencing is not straightforward. PMID:25955497
Suárez-Vega, Aroa; Gutiérrez-Gil, Beatriz; Benavides, Julio; Perez, Valentín; Tosser-Klopp, Gwenola; Klopp, Christophe; Keennel, Stephen J.; Arranz, Juan José
2015-01-01
In this study, we demonstrate the use of a genome-wide association mapping together with RNA-seq in a reduced number of samples, as an efficient approach to detect the causal mutation for a Mendelian disease. Junctional epidermolysis bullosa is a recessive genodermatosis that manifests with neonatal mechanical fragility of the skin, blistering confined to the lamina lucida of the basement membrane and severe alteration of the hemidesmosomal junctions. In Spanish Churra sheep, junctional epidermolysis bullosa (JEB) has been detected in two commercial flocks. The JEB locus was mapped to Ovis aries chromosome 11 by GWAS and subsequently fine-mapped to an 868-kb homozygous segment using the identical-by-descent method. The ITGB4, which is located within this region, was identified as the best positional and functional candidate gene. The RNA-seq variant analysis enabled us to discover a 4-bp deletion within exon 33 of the ITGB4 gene (c.4412_4415del). The c.4412_4415del mutation causes a frameshift resulting in a premature stop codon at position 1472 of the integrin β4 protein. A functional analysis of this deletion revealed decreased levels of mRNA in JEB skin samples and the absence of integrin β4 labeling in immunohistochemical assays. Genotyping of c.4412_4415del showed perfect concordance with the recessive mode of the disease phenotype. Selection against this causal mutation will now be used to solve the problem of JEB in flocks of Churra sheep. Furthermore, the identification of the ITGB4 mutation means that affected sheep can be used as a large mammal animal model for the human form of epidermolysis bullosa with aplasia cutis. Our approach evidences that RNA-seq offers cost-effective alternative to identify variants in the species in which high resolution exome-sequencing is not straightforward. PMID:25955497
Investigating the functional role of callosal connections with dynamic causal models
Stephan, Klaas E.; Penny, Will D.; Marshall, John C.; Fink, Gereon R.; Friston, Karl J.
2009-01-01
The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human post-mortem experiments are currently complemented by diffusion-weighted imaging, which enables non-invasive investigations of callosal connectivity to be conducted. In contrast to the wealth of anatomical data, little is known about the principles by which inter-hemispheric integration is mediated by callosal connections. Most importantly, we lack insights into the mechanisms that determine the functional role of callosal connections in a context-dependent fashion. These mechanisms can now be disclosed by models of effective connectivity that explain neuroimaging data from paradigms which manipulate inter-hemispheric interactions. In this article, we demonstrate that Dynamic Causal Modeling (DCM), in conjunction with Bayesian model selection (BMS), is a powerful approach to disentangling the various factors that determine the functional role of callosal connections. We first review the theoretical foundations of DCM and BMS before demonstrating the application of these techniques to empirical data from a single subject. PMID:16394145
Hume, Mill, Hill, and the sui generis epidemiologic approach to causal inference.
Morabia, Alfredo
2013-11-15
The epidemiologic approach to causal inference (i.e., Hill's viewpoints) consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observational, or experimental epidemiologic studies; and 2) reviews of nonepidemiologic evidence. It does not involve statements of statistical significance. The philosophical roots of Hill's viewpoints are unknown. Superficially, they seem to descend from the ideas of Hume and Mill. Hill's viewpoints, however, use a different kind of evidence and have different purposes than do Hume's rules or Mill's system of logic. In a nutshell, Hume ignores comparative evidence central to Hill's viewpoints. Mill's logic disqualifies as invalid nonexperimental evidence, which forms the bulk of epidemiologic findings reviewed from Hill's viewpoints. The approaches by Hume and Mill cannot corroborate successful implementations of Hill's viewpoints. Besides Hume and Mill, the epidemiologic literature is clueless about a plausible, pre-1965 philosophical origin of Hill's viewpoints. Thus, Hill's viewpoints may be philosophically novel, sui generis, still waiting to be validated and justified. PMID:24071010
Hume, Mill, Hill, and the Sui Generis Epidemiologic Approach to Causal Inference
Morabia, Alfredo
2013-01-01
The epidemiologic approach to causal inference (i.e., Hill's viewpoints) consists of evaluating potential causes from the following 2, noncumulative angles: 1) established results from comparative, observational, or experimental epidemiologic studies; and 2) reviews of nonepidemiologic evidence. It does not involve statements of statistical significance. The philosophical roots of Hill's viewpoints are unknown. Superficially, they seem to descend from the ideas of Hume and Mill. Hill's viewpoints, however, use a different kind of evidence and have different purposes than do Hume's rules or Mill's system of logic. In a nutshell, Hume ignores comparative evidence central to Hill's viewpoints. Mill's logic disqualifies as invalid nonexperimental evidence, which forms the bulk of epidemiologic findings reviewed from Hill's viewpoints. The approaches by Hume and Mill cannot corroborate successful implementations of Hill's viewpoints. Besides Hume and Mill, the epidemiologic literature is clueless about a plausible, pre-1965 philosophical origin of Hill's viewpoints. Thus, Hill's viewpoints may be philosophically novel, sui generis, still waiting to be validated and justified. PMID:24071010
Implications of Three Causal Models for the Measurement of Halo Error.
ERIC Educational Resources Information Center
Fisicaro, Sebastiano A.; Lance, Charles E.
1990-01-01
Three conceptual definitions of halo error are reviewed in the context of causal models of halo error. A corrected correlational measurement of halo error is derived, and the traditional and corrected measures are compared empirically for a 1986 study of 52 undergraduate students' ratings of a lecturer's performance. (SLD)
Dropouts and Turnover: The Synthesis and Test of a Causal Model of Student Attrition.
ERIC Educational Resources Information Center
Bean, John P.
1980-01-01
The determinants of student attrition in higher education institutions are investigated using a causal model which synthesized research findings on job turnover and on student attrition. Many male/female differences were found but three surrogate measures for pay were found for both sexes to be related to intent to leave. (Author/LC)
Using an Analogy To Model Causal Mechanisms in a Complex Text.
ERIC Educational Resources Information Center
Clement, Catherine A.; Yanowitz, Karen L.
2003-01-01
Two experiments examined the role of analogy in the development of a situation model of a target passage, specifically, whether an analogous source text could improve comprehension and inferencing about causal mechanisms in the target. Responses of analogy subjects were more likely to include given and inferred information that comprised the…
Pretense, Counterfactuals, and Bayesian Causal Models: Why What Is Not Real Really Matters
ERIC Educational Resources Information Center
Weisberg, Deena S.; Gopnik, Alison
2013-01-01
Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative…
Causal Analysis to Enhance Creative Problem-Solving: Performance and Effects on Mental Models
ERIC Educational Resources Information Center
Hester, Kimberly S.; Robledo, Issac C.; Barrett, Jamie D.; Peterson, David R.; Hougen, Dean P.; Day, Eric A.; Mumford, Michael D.
2012-01-01
In recent years, it has become apparent that knowledge is a critical component of creative thought. One form of knowledge that might be particularly important to creative thought relies on the mental models people employ to understand novel, ill-defined problems. In this study, undergraduates were given training in the use of causal relationships…
ERIC Educational Resources Information Center
Liu, Jiangang; Li, Jun; Rieth, Cory A.; Huber, David E.; Tian, Jie; Lee, Kang
2011-01-01
The present study employed dynamic causal modeling to investigate the effective functional connectivity between regions of the neural network involved in top-down letter processing. We used an illusory letter detection paradigm in which participants detected letters while viewing pure noise images. When participants detected letters, the response…
The Effects of Time-Limitations and Peer Relationships on Adult Student Learning: A Causal Model.
ERIC Educational Resources Information Center
Lundberg, Carol
Using data from 4,644 undergraduates, this study tested a causal model identifying effects of social integration, age, and time limiting characteristics on adult student learning. Time limiting characteristics included such constraints as off-campus responsibilities and relationships. Educationally related peer relationships were the strongest…
The Impact of School Leadership on School Level Factors: Validation of a Causal Model
ERIC Educational Resources Information Center
Kruger, Meta L.; Witziers, Bob; Sleegers, Peter
2007-01-01
This study aims to contribute to a better understanding of the antecedents and effects of educational leadership, and of the influence of the principal's leadership on intervening and outcome variables. A path analysis was conducted to test and validate a causal model. The results show no direct or indirect effects of educational leadership on…
Causal Comparative Analysis: Comprehensive Literacy Approach or the Traditional Reading Approach
ERIC Educational Resources Information Center
Fuda, Jessica Ann
2009-01-01
A comparative analysis study, examining the significance in reading achievement between students in the Comprehensive Literacy Program to students in the Traditional Basal Reading Approach was conducted. Implementation of the Comprehensive Literacy Program was an effort to lessen the achievement gap between proficient and low progressing students.…
The Epstein–Glaser causal approach to the light-front QED{sub 4}. II: Vacuum polarization tensor
Bufalo, R.; Pimentel, B.M.; Soto, D.E.
2014-12-15
In this work we show how to construct the one-loop vacuum polarization for light-front QED{sub 4} in the framework of the perturbative causal theory. Usually, in the canonical approach, it is considered for the fermionic propagator the so-called instantaneous term, but it is known in the literature that this term is controversial because it can be omitted by computational reasons; for instance, by compensation or vanishing by dimensional regularization. In this work we propose a solution to this paradox. First, in the Epstein–Glaser causal theory, it is shown that the fermionic propagator does not have instantaneous term, and with this propagator we calculate the one-loop vacuum polarization, from this calculation it follows the same result as those obtained by the standard approach, but without reclaiming any extra assumptions. Moreover, since the perturbative causal theory is defined in the distributional framework, we can also show the reason behind our obtaining the same result whether we consider or not the instantaneous fermionic propagator term. - Highlights: • We develop the Epstein–Glaser causal approach for light-front field theory. • We evaluate in detail the vacuum polarization at one-loop for the light-front QED. • We discuss the subtle issues of the Instantaneous part of the fermionic propagator in the light-front. • We evaluate the vacuum polarization at one-loop for the light-front QED with the Instantaneous fermionic part.
Causal Modeling for Indirect Cost Management at Universities and Colleges.
ERIC Educational Resources Information Center
Muir, Albert E.
1980-01-01
The logical sequence of one set of propositions regarding the association among proposal output, negotiated indirect cost rates, and overhead recovery, using a sample of 24 institutions of higher education within the SUNY system, is investigated. A model for estimating overhead a year in advance is presented. (Author/MLW)
Scheungrab, M
1990-01-01
The subject of research coucerns causal relationships between variables of consuming home videos and television and different indicators of delinquency ("acceptance of social norms" (NORM-AK), "perceived risk of punishment" (DEL-RISK), "severity of negative consequences" (NEG-VAL), "acceptance of illegitimate means" (ILLEG-M)). Additionally, factors of influence external to media are taken into consideration which are connected with delinquency according to criminologic results, i.e. variables of communication and variables of the family life and the structure of the family. The model is tested by a sample of N = 305 male pupils of a Regensburg vocational school with methods analysing causality ("2-Stage-Least-Square" (2-SLS) and "Latent variables path analysis with partial least squares estimation" (LVPLS)). The 2-SLS-estimates largely confirm the causal relationships supposed in the model. The results are, three significantly positive indirect connections from the preference for violence of home videos to the main indicator of delinquency ILLEG-M (by way of the variables "consumption of home videos" put on the Index, NEG-VAL and DEL-RISK). The direct influence of the preference for violence on television on ILLEG-M is confirmed, whereas the direct path from the popularity of violent video films to ILLEG-M cannot be proved. The LVPLS-results essentially correspond to the relationship shown by 2-SLS; in addition the LVPLS-estimates also confirm direct causal relationships between the latent variables "consumption of violent video films" and "delinquency proneness". PMID:2132917
2013-01-01
Drug-induced cardiac toxicity has been implicated in 31% of drug withdrawals in the USA. The fact that the risk for cardiac-related adverse events goes undetected in preclinical studies for so many drugs underscores the need for better, more predictive in vitro safety screens to be deployed early in the drug discovery process. Unfortunately, many questions remain about the ability to accurately translate findings from simple cellular systems to the mechanisms that drive toxicity in the complex in vivo environment. In this study, we analyzed translatability of cardiotoxic effects for a diverse set of drugs from rodents to two different cell systems (rat heart tissue-derived cells (H9C2) and primary rat cardiomyocytes (RCM)) based on their transcriptional response. To unravel the altered pathway, we applied a novel computational systems biology approach, the Causal Reasoning Engine (CRE), to infer upstream molecular events causing the observed gene expression changes. By cross-referencing the cardiotoxicity annotations with the pathway analysis, we found evidence of mechanistic convergence towards common molecular mechanisms regardless of the cardiotoxic phenotype. We also experimentally verified two specific molecular hypotheses that translated well from in vivo to in vitro (Kruppel-like factor 4, KLF4 and Transforming growth factor beta 1, TGFB1) supporting the validity of the predictions of the computational pathway analysis. In conclusion, this work demonstrates the use of a novel systems biology approach to predict mechanisms of toxicity such as KLF4 and TGFB1 that translate from in vivo to in vitro. We also show that more complex in vitro models such as primary rat cardiomyocytes may not offer any advantage over simpler models such as immortalized H9C2 cells in terms of translatability to in vivo effects if we consider the right endpoints for the model. Further assessment and validation of the generated molecular hypotheses would greatly enhance our ability to
Causality issues of particle detector models in QFT and quantum optics
NASA Astrophysics Data System (ADS)
Martín-Martínez, Eduardo
2015-11-01
We analyze the constraints that causality imposes on some of the particle detector models employed in quantum field theory in general and, in particular, on those used in quantum optics (or superconducting circuits) to model atoms interacting with light. Namely, we show that disallowing faster-than-light communication can impose severe constraints on the applicability of particle detector models in three different common scenarios: (1) when the detectors are spatially smeared, (2) when a UV cutoff is introduced in the theory and (3) under one of the most typical approximations made in quantum optics: the rotating-wave approximation. We identify the scenarios in which the models' causal behavior can and cannot be cured.
Hernán, Miguel A
2015-01-15
The relative weights of empirical facts (data) and assumptions (theory) in causal inference vary across disciplines. Typically, disciplines that ask more complex questions tend to better tolerate a greater role of theory and modeling in causal inference. As epidemiologists move toward increasingly complex questions, Marshall and Galea (Am J Epidemiol. 2015;181(2):92-99) support a reweighting of data and theory in epidemiologic research via the use of agent-based modeling. The parametric g-formula can be viewed as an intermediate step between traditional epidemiologic methods and agent-based modeling and therefore is a method that can ease the transition toward epidemiologic methods that rely heavily on modeling. PMID:25480820
Assessing parameter identifiability for dynamic causal modeling of fMRI data.
Arand, Carolin; Scheller, Elisa; Seeber, Benjamin; Timmer, Jens; Klöppel, Stefan; Schelter, Björn
2015-01-01
Deterministic dynamic causal modeling (DCM) for fMRI data is a sophisticated approach to analyse effective connectivity in terms of directed interactions between brain regions of interest. To date it is difficult to know if acquired fMRI data will yield precise estimation of DCM parameters. Focusing on parameter identifiability, an important prerequisite for research questions on directed connectivity, we present an approach inferring if parameters of an envisaged DCM are identifiable based on information from fMRI data. With the freely available "attention to motion" dataset, we investigate identifiability of two DCMs and show how different imaging specifications impact on identifiability. We used the profile likelihood, which has successfully been applied in systems biology, to assess the identifiability of parameters in a DCM with specified scanning parameters. Parameters are identifiable when minima of the profile likelihood as well as finite confidence intervals for the parameters exist. Intermediate epoch duration, shorter TR and longer session duration generally increased the information content in the data and thus improved identifiability. Irrespective of biological factors such as size and location of a region, attention should be paid to densely interconnected regions in a DCM, as those seem to be prone to non-identifiability. Our approach, available in the DCMident toolbox, enables to judge if the parameters of an envisaged DCM are sufficiently determined by underlying data without priors as opposed to primarily reflecting the Bayesian priors in a SPM-DCM. Assessments with the DCMident toolbox prior to a study will lead to improved identifiability of the parameters and thus might prevent suboptimal data acquisition. Thus, the toolbox can be used as a preprocessing step to provide immediate statements on parameter identifiability. PMID:25750612
Gradient-free MCMC methods for dynamic causal modelling
Sengupta, Biswa; Friston, Karl J.; Penny, Will D.
2015-01-01
In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling; yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density — albeit at almost 1000% increase in computational time, in comparison to the most efficient algorithm (i.e., the adaptive MCMC sampler). PMID:25776212
Edge replacement and minimality as models of causal inference in children.
Buchanan, David W; Sobel, David M
2014-01-01
Recently, much research has focused on causal graphical models (CGMs) as a computational-level description of how children represent cause and effect. While this research program has shown promise, there are aspects of causal reasoning that CGMs have difficulty accommodating. We propose a new formalism that amends CGMs. This edge replacement grammar formalizes one existing and one novel theoretical commitment. The existing idea is that children are determinists, in the sense that they believe that apparent randomness comes from hidden complexity, rather than inherent nondeterminism in the world. The new idea is that children think of causation as a branching process: causal relations grow not directly from the cause, but from existing relations between the cause and other effects. We have shown elsewhere that these two commitments together, when formalized, can explain and quantitatively fit the otherwise puzzling effect of nonindependence observed in the adult causal reasoning literature. We then test the qualitative predictions of this new formalism on children in a series of three experiments. PMID:24851350
Little, E.E.; Bridges, C.M.; Linder, G.; Boone, M.
2003-01-01
Research to date has indicated that a range of environmental variables such as disease, parasitism, predation, competition, environmental contamination, solar ultraviolet radiation, climate change, or habitat alteration may be responsible for declining amphibian populations and the appearance of deformed organisms, yet in many cases no definitive environmental variable stands out as a causal factor. Multiple Stressors are often present in the habitat, and interactions among these can magnify injury to biota. This raises the possibility that the additive or synergistic impact of these Stressors may be the underlying cause of amphibian declines. Effective management for the restoration of amphibian populations requires the identification of causal factors contributing to their declines. A systematic approach to determine causality is especially important because initial impressions may be misleading or ambiguous. In addition, the evaluation of amphibian populations requires consideration of a broader spatial scale than commonly used in regulatory monitoring. We describe a systematic three-tiered approach to determine causality in amphibian declines and deformities. Tier 1 includes an evaluation of historic databases and extant data and would involve a desktop synopsis of the status of various stressors as well as site visits. Tier 2 studies are iterative, hypothesis driven studies beginning with general tests and continuing with analyses of increasing complexity as certain stressors are identified for further investigation. Tier 3 applies information developed in Tier 2 as predictive indicators of habitats and species at risk over broad landscape scales and provides decision support for the adaptive management of amphibian recovery. This comprehensive, tiered program could provide a mechanistic approach to identifying and addressing specific stressors responsible for amphibian declines across various landscapes.
Modeling and Encoding Clinical Causal Relationships in a Medical Knowledge Base
Blum, Robert L.
1983-01-01
This paper presents a method for the computer modeling and encoding of clinical causal relationships (CR's). This method draws on the theory of multivariate linear models and path analysis. The representation was used to encode medical CR's derived empirically from a clinical database by the RX computer project described in SCAMC82. The emphasis in the representation is on capturing the intensities of effects and the variation in the effects across a patient population. This information is used by RX in determining the validity of other CR's. The representation uses a directed graph formalism in which the nodes are frames and the arcs contain seven descriptive features of individual CR's: intensity, distribution, direction, mathematical form, setting, validity, and evidence. Because natural systems (such as the human body) are inherently probabilistic, linear models are useful in representing causal flow in them.
NASA Astrophysics Data System (ADS)
Chee-Yin, Yip; Hock-Eam, Lim
2014-12-01
This paper examines using housing supply as proxy to house prices, the causal relationship on house prices among 8 states in Malaysia by applying the Engle-Granger cointegration test and Granger causality test approach. The target states are Perak, Selangor, Penang, Federal Territory of Kuala Lumpur (WPKL or Kuala Lumpur), Kedah, Negeri Sembilan, Sabah and Sarawak. The primary aim of this study is to estimate how long (in months) house prices in Perak lag behind that of Selangor, Penang and WPKL. We classify the 8 states into two categories - developed and developing states. We use Engle-Granger cointegration test and Granger causality test to examine the long run and short run equilibrium relationship among the two categories.. It is found that the causal relationship is bidirectional in Perak and Sabah, Perak and Selangor while it is unidirectional for Perak and Sarawak, Perak and Penang, Perak and WPKL. The speed of deviation adjustment is about 273%, suggesting that the pricing dynamic of Perak has a 32- month or 2 3/4- year lag behind that of WPKL, Selangor and Penang. Such information will be useful to investors, house buyers and speculators.
Recursive causality in evolution: a model for epigenetic mechanisms in cancer development.
Haslberger, A; Varga, F; Karlic, H
2006-01-01
Interactions between adaptative and selective processes are illustrated in the model of recursive causality as defined in Rupert Riedl's systems theory of evolution. One of the main features of this theory also termed as theory of evolving complexity is the centrality of the notion of 'recursive' or 'feedback' causality - 'the idea that every biological effect in living systems, in some way, feeds back to its own cause'. Our hypothesis is that "recursive" or "feedback" causality provides a model for explaining the consequences of interacting genetic and epigenetic mechanisms which are known to play a key role in development of cancer. Epigenetics includes any process that alters gene activity without changes of the DNA sequence. The most important epigenetic mechanisms are DNA-methylation and chromatin remodeling. Hypomethylation of so-called oncogenes and hypermethylation of tumor suppressor genes appear to be critical determinants of cancer. Folic acid, vitamin B12 and other nutrients influence the function of enzymes that participate in various methylation processes by affecting the supply of methyl groups into a variety of molecules which may be directly or indirectly associated with cancerogenesis. We present an example from our own studies by showing that vitamin D3 has the potential to de-methylate the osteocalcin-promoter in MG63 osteosarcoma cells. Consequently, a stimulation of osteocalcin synthesis can be observed. The above mentioned enzymes also play a role in development and differentiation of cells and organisms and thus illustrate the close association between evolutionary and developmental mechanisms. This enabled new ways to understand the interaction between the genome and environment and may improve biomedical concepts including environmental health aspects where epigenetic and genetic modifications are closely associated. Recent observations showed that methylated nucleotides in the gene promoter may serve as a target for solar UV
Causal Responsibility and Counterfactuals
Lagnado, David A; Gerstenberg, Tobias; Zultan, Ro'i
2013-01-01
How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions. PMID:23855451
Bassani, Tito; Bari, Vlasta; Marchi, Andrea; Tassin, Stefano; Dalla Vecchia, Laura; Canesi, Margherita; Barbic, Franca; Furlan, Raffaello; Porta, Alberto
2014-07-01
We tested the hypothesis that causality analysis, applied to the spontaneous beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP), can identify the improvement of autonomic control linked to plantar mechanical stimulation in patients with Parkinson's disease (PD). A causality index, measuring the strength of the association from SAP to HP variability, and derived according to the Granger paradigm (i.e. SAP causes HP if the inclusion of SAP into the set of signals utilized to describe cardiovascular interactions improves the prediction of HP series), was calculated using both linear model-based (MB) and nonlinear model-free (MF) approaches. Univariate HP and SAP variability indices in time and frequency domains, and bivariate descriptors of the HP-SAP variability interactions were computed as well. We studied ten PD patients (age range: 57-78 years; Hoehn-Yahr scale: 2-3; six males, four females) without orthostatic hypotension or symptoms of orthostatic intolerance and 'on-time' according to their habitual pharmacological treatment. PD patients underwent recordings at rest in a supine position and during a head-up tilt before, and 24 h after, mechanical stimulation was applied to the plantar surface of both feet. The MF causality analysis indicated a greater involvement of baroreflex in regulating HP-SAP variability interactions after mechanical stimulation. Remarkably, MB causality and more traditional univariate or bivariate techniques could not detect changes in cardiovascular regulation after mechanical stimulation, thus stressing the importance of accounting for nonlinear dynamics in PD patients. Due to the higher statistical power of MF causality we suggest its exploitation to monitor the baroreflex control improvement in PD patients, and we encourage the clinical application of the Granger causality approach to evaluate the modification of the autonomic control in relation to the application of a pharmacological treatment, a
From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology
Eisenhauer, Nico; Powell, Jeff R; Grace, James B.; Bowker, Matthew A.
2015-01-01
In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.
A Bayesian network approach for causal inferences in pesticide risk assessment and management
Pesticide risk assessment and management must balance societal benefits and ecosystem protection, based on quantified risks and the strength of the causal linkages between uses of the pesticide and socioeconomic and ecological endpoints of concern. A Bayesian network (BN) is a gr...
Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan
2015-01-01
This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129
Schnitzer, Mireille E; Lok, Judith J; Gruber, Susan
2016-05-01
This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010 [27]) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low- and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129
Exploratory Causal Analysis in Bivariate Time Series Data
NASA Astrophysics Data System (ADS)
McCracken, James M.
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data
Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model.
Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van den Berg, Helma; Conner, Mark; van der Maas, Han L J
2016-01-01
This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of "shortcuts" between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study. PMID:26479706
Stochastic dynamic causal modelling of fMRI data: Should we care about neural noise?
Daunizeau, J.; Stephan, K.E.; Friston, K.J.
2012-01-01
Dynamic causal modelling (DCM) was introduced to study the effective connectivity among brain regions using neuroimaging data. Until recently, DCM relied on deterministic models of distributed neuronal responses to external perturbation (e.g., sensory stimulation or task demands). However, accounting for stochastic fluctuations in neuronal activity and their interaction with task-specific processes may be of particular importance for studying state-dependent interactions. Furthermore, allowing for random neuronal fluctuations may render DCM more robust to model misspecification and finesse problems with network identification. In this article, we examine stochastic dynamic causal models (sDCM) in relation to their deterministic counterparts (dDCM) and highlight questions that can only be addressed with sDCM. We also compare the network identification performance of deterministic and stochastic DCM, using Monte Carlo simulations and an empirical case study of absence epilepsy. For example, our results demonstrate that stochastic DCM can exploit the modelling of neural noise to discriminate between direct and mediated connections. We conclude with a discussion of the added value and limitations of sDCM, in relation to its deterministic homologue. PMID:22579726
NASA Astrophysics Data System (ADS)
Komperda, Regis
The purpose of this dissertation is to test a model of relationships among factors characterizing aspects of a student-centered constructivist learning environment and student outcomes of satisfaction and academic achievement in introductory undergraduate chemistry courses. Constructivism was chosen as the theoretical foundation for this research because of its widespread use in chemical education research and practice. In a constructivist learning environment the role of the teacher shifts from delivering content towards facilitating active student engagement in activities that encourage individual knowledge construction through discussion and application of content. Constructivist approaches to teaching introductory chemistry courses have been adopted by some instructors as a way to improve student outcomes, but little research has been done on the causal relationships among particular aspects of the learning environment and student outcomes. This makes it difficult for classroom teachers to know which aspects of a constructivist teaching approach are critical to adopt and which may be modified to better suit a particular learning environment while still improving student outcomes. To investigate a model of these relationships, a survey designed to measure student perceptions of three factors characterizing a constructivist learning environment in online courses was adapted for use in face-to-face chemistry courses. These three factors, teaching presence, social presence, and cognitive presence, were measured using a slightly modified version of the Community of Inquiry (CoI) instrument. The student outcomes investigated in this research were satisfaction and academic achievement, as measured by standardized American Chemical Society (ACS) exam scores and course grades. Structural equation modeling (SEM) was used to statistically model relationships among the three presence factors and student outcome variables for 391 students enrolled in six sections of a
Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir
2014-01-01
This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817
Hu, Zhenghui; Ni, Pengyu; Wan, Qun; Zhang, Yan; Shi, Pengcheng; Lin, Qiang
2016-01-01
Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known as V0 in hemodynamic models. In previous studies involving dynamic causal modeling (DCM) which embodies the hemodynamic model to invert the functional magnetic resonance imaging signals into neuronal activity, V0 was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how the V0 value influences DCM. In this study we addressed this issue by using both synthetic and real experiments. The results show that the ability of DCM analysis to reveal information about brain causality depends critically on the assumed V0 value used in the analysis procedure. The choice of V0 value not only directly affects the strength of system connections, but more importantly also affects the inferences about the network architecture. Our analyses speak to a possible refinement of how the hemody-namic process is parameterized (i.e., by making V0 a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realistic V0 information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity. PMID:27389074
Hu, Zhenghui; Ni, Pengyu; Wan, Qun; Zhang, Yan; Shi, Pengcheng; Lin, Qiang
2016-01-01
Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known as V0 in hemodynamic models. In previous studies involving dynamic causal modeling (DCM) which embodies the hemodynamic model to invert the functional magnetic resonance imaging signals into neuronal activity, V0 was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how the V0 value influences DCM. In this study we addressed this issue by using both synthetic and real experiments. The results show that the ability of DCM analysis to reveal information about brain causality depends critically on the assumed V0 value used in the analysis procedure. The choice of V0 value not only directly affects the strength of system connections, but more importantly also affects the inferences about the network architecture. Our analyses speak to a possible refinement of how the hemody-namic process is parameterized (i.e., by making V0 a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realistic V0 information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity. PMID:27389074
The Reactive-Causal Architecture: Introducing an Emotion Model along with Theories of Needs
NASA Astrophysics Data System (ADS)
Aydin, Ali Orhan; Orgun, Mehmet Ali
In the entertainment application area, one of the major aims is to develop believable agents. To achieve this aim, agents should be highly autonomous, situated, flexible, and display affect. The Reactive-Causal Architecture (ReCau) is proposed to simulate these core attributes. In its current form, ReCau cannot explain the effects of emotions on intelligent behaviour. This study aims is to further improve the emotion model of ReCau to explain the effects of emotions on intelligent behaviour. This improvement allows ReCau to be emotional to support the development of believable agents.
Nelson, Suchitra; Albert, Jeffrey M.
2013-01-01
Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a non-zero total mediation effect increases as the correlation coefficient between two mediators increases, while power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated. PMID:23650048
Streja, Elani; Park, Jongha; Chan, Ting-Yan; Lee, Janet; Soohoo, Melissa; Rhee, Connie M; Arah, Onyebuchi A; Kalantar-Zadeh, Kamyar
2016-01-01
It has been previously reported that a higher erythropoiesis stimulating agent (ESA) dose in hemodialysis patients is associated with adverse outcomes including mortality; however the causal relationship between ESA and mortality is still hotly debated. We hypothesize ESA dose indeed exhibits a direct linear relationship with mortality in models of association implementing the use of a marginal structural model (MSM), which controls for time-varying confounding and examines causality in the ESA dose-mortality relationship. We conducted a retrospective cohort study of 128 598 adult hemodialysis patients over a 5-year follow-up period to evaluate the association between weekly ESA (epoetin-α) dose and mortality risk. A MSM was used to account for baseline and time-varying covariates especially laboratory measures including hemoglobin level and markers of malnutrition-inflammation status. There was a dose-dependent positive association between weekly epoetin-α doses ≥18 000 U/week and mortality risk. Compared to ESA dose of <6 000 U/week, adjusted odds ratios (95% confidence interval) were 1.02 (0.94-1.10), 1.08 (1.00-1.18), 1.17 (1.06-1.28), 1.27 (1.15-1.41), and 1.52 (1.37-1.69) for ESA dose of 6 000 to <12 000, 12 000 to <18 000, 18 000 to <24 000, 24 000 to <30 000, and ≥30 000 U/week, respectively. High ESA dose may be causally associated with excessive mortality, which is supportive of guidelines which advocate for conservative management of ESA dosing regimen in hemodialysis patients. PMID:27298736
Streja, Elani; Park, Jongha; Chan, Ting-Yan; Lee, Janet; Soohoo, Melissa; Rhee, Connie M.; Arah, Onyebuchi A.; Kalantar-Zadeh, Kamyar
2016-01-01
It has been previously reported that a higher erythropoiesis stimulating agent (ESA) dose in hemodialysis patients is associated with adverse outcomes including mortality; however the causal relationship between ESA and mortality is still hotly debated. We hypothesize ESA dose indeed exhibits a direct linear relationship with mortality in models of association implementing the use of a marginal structural model (MSM), which controls for time-varying confounding and examines causality in the ESA dose-mortality relationship. We conducted a retrospective cohort study of 128 598 adult hemodialysis patients over a 5-year follow-up period to evaluate the association between weekly ESA (epoetin-α) dose and mortality risk. A MSM was used to account for baseline and time-varying covariates especially laboratory measures including hemoglobin level and markers of malnutrition-inflammation status. There was a dose-dependent positive association between weekly epoetin-α doses ≥18 000 U/week and mortality risk. Compared to ESA dose of <6 000 U/week, adjusted odds ratios (95% confidence interval) were 1.02 (0.94–1.10), 1.08 (1.00–1.18), 1.17 (1.06–1.28), 1.27 (1.15–1.41), and 1.52 (1.37–1.69) for ESA dose of 6 000 to <12 000, 12 000 to <18 000, 18 000 to <24 000, 24 000 to <30 000, and ≥30 000 U/week, respectively. High ESA dose may be causally associated with excessive mortality, which is supportive of guidelines which advocate for conservative management of ESA dosing regimen in hemodialysis patients. PMID:27298736
Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy
Papadopoulou, Margarita; Leite, Marco; van Mierlo, Pieter; Vonck, Kristl; Lemieux, Louis; Friston, Karl; Marinazzo, Daniele
2015-01-01
In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space — identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory–inhibitory balance. PMID:25498428
Darwin's diagram of divergence of taxa as a causal model for the origin of species.
Bouzat, Juan L
2014-03-01
On the basis that Darwin's theory of evolution encompasses two logically independent processes (common descent and natural selection), the only figure in On the Origin of Species (the Diagram of Divergence of Taxa) is often interpreted as illustrative of only one of these processes: the branching patterns representing common ancestry. Here, I argue that Darwin's Diagram of Divergence of Taxa represents a broad conceptual model of Darwin's theory, illustrating the causal efficacy of natural selection in producing well-defined varieties and ultimately species. The Tree Diagram encompasses the idea that natural selection explains common descent and the origin of organic diversity, thus representing a comprehensive model of Darwin's theory on the origin of species. I describe Darwin's Tree Diagram in relation to his argumentative strategy under the vera causa principle, and suggest that the testing of his theory based on the evidence from the geological record, the geographical distribution of organisms, and the mutual affinities of organic beings can be framed under the hypothetico-deductive method. Darwin's Diagram of Divergence of Taxa therefore represents a broad conceptual model that helps understanding the causal construction of Darwin's theory of evolution, the structure of his argumentative strategy, and the nature of his scientific methodology. PMID:24672902
The Epstein-Glaser causal approach to the light-front QED4. I: Free theory
NASA Astrophysics Data System (ADS)
Bufalo, R.; Pimentel, B. M.; Soto, D. E.
2014-12-01
In this work we present the study of light-front field theories in the realm of the axiomatic theory. It is known that when one uses the light-cone gauge pathological poles (k+) - n arises, demanding a prescription to be employed in order to tame these ill-defined poles and to have the correct Feynman integrals due to the lack of Wick rotation in such theories. In order to shed a new light on this long standing problem we present here a discussion based on the use of rigorous mathematical machinery of the distributional theory combined with physical concepts, such as causality, to show how to deal with these singular propagators in a general fashion without making use of any prescription. The first step of our development will consist in showing how the analytic representation for propagators arises by requiring general physical properties within the framework of Wightman's formalism. From that we shall determine the equal-time (anti)commutation relations in the light-front form for the scalar and fermionic fields, as well as for the dynamical components of the electromagnetic field. In conclusion, we introduce the Epstein-Glaser causal method in order to have a mathematical rigorous description of the free propagators of the theory, allowing us to discuss a general treatment for propagators of the type (k+) - n. Afterwards, we show that at given conditions our results reproduce known prescriptions in the literature.
Promoting the organ donor card: a causal model of persuasion effects.
Skumanich, S A; Kintsfather, D P
1996-08-01
Due to the present critical shortage of donor organs available for transplantation, effective communication strategies are necessary to heighten public commitment to donation. The promotion of organ donor card-signing may be a successful vehicle in the achievement of this goal. Based on the Elaboration Likelihood Model of persuasion effects, evidence of the motivation for organ donor card-signing, and examination of previous donation message tests, this study proposes and tests a causal model of response to organ donor card appeals. The inter-relationship of values, empathy arousal, and issue involvement was found to be a significant driving force in the persuasive process for the behavioral intention to sign an organ donor card. Implications of these findings for future research are addressed. PMID:8844941
Causal modeling of secondary science students' intentions to enroll in physics
NASA Astrophysics Data System (ADS)
Crawley, Frank E.; Black, Carolyn B.
The purpose of this study was to explore the utility of the theory of planned behavior model developed by social psychologists for understanding and predicting the behavioral intentions of secondary science students regarding enrolling in physics. In particular, the study used a three-stage causal model to investigate the links from external variables to behavioral, normative, and control beliefs; from beliefs to attitudes, subjective norm, and perceived behavioral control; and from attitudes, subjective norm, and perceived behavioral control to behavioral intentions. The causal modeling method was employed to verify the underlying causes of secondary science students' interest in enrolling physics as predicted in the theory of planned behavior. Data were collected from secondary science students (N = 264) residing in a central Texas city who were enrolled in earth science (8th grade), biology (9th grade), physical science (10th grade), or chemistry (11th grade) courses. Cause-and-effect relationships were analyzed using path analysis to test the direct effects of model variables specified in the theory of planned behavior. Results of this study indicated that students' intention to enroll in a high school physics course was determined by their attitude toward enrollment and their degree of perceived behavioral control. Attitude, subjective norm, and perceived behavioral control were, in turn, formed as a result of specific beliefs that students held about enrolling in physics. Grade level and career goals were found to be instrumental in shaping students' attitude. Immediate family members were identified as major referents in the social support system for enrolling in physics. Course and extracurricular conflicts and the fear of failure were shown to be the primary beliefs obstructing students' perception of control over physics enrollment. Specific recommendations are offered to researchers and practitioners for strengthening secondary school students
Naimi, Ashley I.; Richardson, David B.; Cole, Stephen R.
2013-01-01
In a recent issue of the Journal, Kirkeleit et al. (Am J Epidemiol. 2013;177(11):1218–1224) provided empirical evidence for the potential of the healthy worker effect in a large cohort of Norwegian workers across a range of occupations. In this commentary, we provide some historical context, define the healthy worker effect by using causal diagrams, and use simulated data to illustrate how structural nested models can be used to estimate exposure effects while accounting for the healthy worker survivor effect in 4 simple steps. We provide technical details and annotated SAS software (SAS Institute, Inc., Cary, North Carolina) code corresponding to the example analysis in the Web Appendices, available at http://aje.oxfordjournals.org/. PMID:24077092
Admon, Roee; Milad, Mohammed R; Hendler, Talma
2013-07-01
Discriminating neural abnormalities into the causes versus consequences of psychopathology would enhance the translation of neuroimaging findings into clinical practice. By regarding the traumatic encounter as a reference point for disease onset, neuroimaging studies of post-traumatic stress disorder (PTSD) can potentially allocate PTSD neural abnormalities to either predisposing (pre-exposure) or acquired (post-exposure) factors. Based on novel research strategies in PTSD neuroimaging, including genetic, environmental, twin, and prospective studies, we provide a causal model that accounts for neural abnormalities in PTSD, and outline its clinical implications. Current data suggest that abnormalities within the amygdala and dorsal anterior cingulate cortex represent predisposing risk factors for developing PTSD, whereas dysfunctional hippocampal-ventromedial prefrontal cortex (vmPFC) interactions may become evident only after having developed the disorder. PMID:23768722
Carmack, Chakema C.; Lewis-Moss, Rhonda K.
2010-01-01
The authors investigated whether a causal-indicator model or an effect-indicator model of the theory of planned behavior (TPB) is more suitable for predicting behavioral intention and for which behaviors. No previous studies have evaluated this question using the same sample and same behavior. In this study, African American adolescents ages 12–17 participating in risk reduction classes were assessed on their initial attitudes, norms, perceived control, and intention regarding condom use. Second-order structural equation modeling indicated that the effect-indicator model exhibited superior fit above the causal-indicator model. Furthermore, modeling the behavioral antecedents in a causal way may not be as accurate due to the underlying uni-dimensional nature of attitudes, subjective norms, and control. The TPB was not disconfirmed as a suitable model for African American adolescents’ regarding condom use. Prevention programs may benefit by focusing on adolescent behavior change with regard to the global components in order to influence more specific concepts of these social cognitions. Editors’ Strategic Implications: Despite limitations including correlational data, this study yields implications for prevention programming and, more broadly, an important theoretical elaboration on effect-indicator and causal-indicator models of the TPB. PMID:19949867
ERIC Educational Resources Information Center
Tighe, Elizabeth L.; Wagner, Richard K.; Schatschneider, Christopher
2015-01-01
This study demonstrates the utility of applying a causal indicator modeling framework to investigate important predictors of reading comprehension in third, seventh, and tenth grade students. The results indicated that a 4-factor multiple indicator multiple indicator cause (MIMIC) model of reading comprehension provided adequate fit at each grade…
NASA Astrophysics Data System (ADS)
Tsumura, Kyosuke; Kikuchi, Yuta; Kunihiro, Teiji
2015-10-01
We derive the second-order hydrodynamic equation and the microscopic formulas of the relaxation times as well as the transport coefficients systematically from the relativistic Boltzmann equation. Our derivation is based on a novel development of the renormalization-group method, a powerful reduction theory of dynamical systems, which has been applied successfully to derive the nonrelativistic second-order hydrodynamic equation. Our theory nicely gives a compact expression of the deviation of the distribution function in terms of the linearized collision operator, which is different from those used as an ansatz in the conventional fourteen-moment method. It is confirmed that the resultant microscopic expressions of the transport coefficients coincide with those derived in the Chapman-Enskog expansion method. Furthermore, we show that the microscopic expressions of the relaxation times have natural and physically plausible forms. We prove that the propagating velocities of the fluctuations of the hydrodynamical variables do not exceed the light velocity, and hence our second-order equation ensures the desired causality. It is also confirmed that the equilibrium state is stable for any perturbation described by our equation.
Wang, Chi; Dominici, Francesca; Parmigiani, Giovanni; Zigler, Corwin Matthew
2015-01-01
Summary Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012) and Lefebvre et al. (2014), we propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of the exposure coefficient in the outcome model due to non-collapsibility. We implement a Bayesian bootstrap procedure to integrate over the distribution of potential confounders and to estimate the causal effect. Our method permits estimation of both the overall population causal effect and effects in specified subpopulations, providing clear characterization of heterogeneous exposure effects that may vary considerably across different covariate profiles. Simulation studies demonstrate that the proposed method performs well in small sample size situations with 100 to 150 observations and 50 covariates. The method is applied to data on 15060 US Medicare beneficiaries diagnosed with a malignant brain tumor between 2000 and 2009 to evaluate whether surgery reduces hospital readmissions within thirty days of diagnosis. PMID:25899155
Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates
ERIC Educational Resources Information Center
Bollen, Kenneth A.; Bauldry, Shawn
2011-01-01
In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of…
Programs as Causal Models: Speculations on Mental Programs and Mental Representation
ERIC Educational Resources Information Center
Chater, Nick; Oaksford, Mike
2013-01-01
Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of…
The Epstein–Glaser causal approach to the light-front QED{sub 4}. I: Free theory
Bufalo, R. Pimentel, B.M. Soto, D.E.
2014-12-15
In this work we present the study of light-front field theories in the realm of the axiomatic theory. It is known that when one uses the light-cone gauge pathological poles (k{sup +}){sup −n} arises, demanding a prescription to be employed in order to tame these ill-defined poles and to have the correct Feynman integrals due to the lack of Wick rotation in such theories. In order to shed a new light on this long standing problem we present here a discussion based on the use of rigorous mathematical machinery of the distributional theory combined with physical concepts, such as causality, to show how to deal with these singular propagators in a general fashion without making use of any prescription. The first step of our development will consist in showing how the analytic representation for propagators arises by requiring general physical properties within the framework of Wightman’s formalism. From that we shall determine the equal-time (anti)commutation relations in the light-front form for the scalar and fermionic fields, as well as for the dynamical components of the electromagnetic field. In conclusion, we introduce the Epstein–Glaser causal method in order to have a mathematical rigorous description of the free propagators of the theory, allowing us to discuss a general treatment for propagators of the type (k{sup +}){sup −n}. Afterwards, we show that at given conditions our results reproduce known prescriptions in the literature. - Highlights: • We develop the analytic representation for propagators in Wightman’s framework. • We make use of the analytic representation to obtain equal-time (anti)commutation relations in the light-front. • We derive the free Feynman propagators for the light-front quantum electrodynamics in the Epstein–Glaser approach. • We determine a general expression for the propagator associated to the light-cone poles (k{sup +}){sup −n} in the causal approach.
Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.
2015-01-01
Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of
Detection of Motor Changes in Huntington's Disease Using Dynamic Causal Modeling.
Minkova, Lora; Scheller, Elisa; Peter, Jessica; Abdulkadir, Ahmed; Kaller, Christoph P; Roos, Raymund A; Durr, Alexandra; Leavitt, Blair R; Tabrizi, Sarah J; Klöppel, Stefan
2015-01-01
Deficits in motor functioning are one of the hallmarks of Huntington's disease (HD), a genetically caused neurodegenerative disorder. We applied functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess changes that occur with disease progression in the neural circuitry of key areas associated with executive and cognitive aspects of motor control. Seventy-seven healthy controls, 62 pre-symptomatic HD gene carriers (preHD), and 16 patients with manifest HD symptoms (earlyHD) performed a motor finger-tapping fMRI task with systematically varying speed and complexity. DCM was used to assess the causal interactions among seven pre-defined regions of interest, comprising primary motor cortex, supplementary motor area (SMA), dorsal premotor cortex, and superior parietal cortex. To capture heterogeneity among HD gene carriers, DCM parameters were entered into a hierarchical cluster analysis using Ward's method and squared Euclidian distance as a measure of similarity. After applying Bonferroni correction for the number of tests, DCM analysis revealed a group difference that was not present in the conventional fMRI analysis. We found an inhibitory effect of complexity on the connection from parietal to premotor areas in preHD, which became excitatory in earlyHD and correlated with putamen atrophy. While speed of finger movements did not modulate the connection from caudal to pre-SMA in controls and preHD, this connection became strongly negative in earlyHD. This second effect did not survive correction for multiple comparisons. Hierarchical clustering separated the gene mutation carriers into three clusters that also differed significantly between these two connections and thereby confirmed their relevance. DCM proved useful in identifying group differences that would have remained undetected by standard analyses and may aid in the investigation of between-subject heterogeneity. PMID:26635585
Detection of Motor Changes in Huntington's Disease Using Dynamic Causal Modeling
Minkova, Lora; Scheller, Elisa; Peter, Jessica; Abdulkadir, Ahmed; Kaller, Christoph P.; Roos, Raymund A.; Durr, Alexandra; Leavitt, Blair R.; Tabrizi, Sarah J.; Klöppel, Stefan; Coleman, A.
2015-01-01
Deficits in motor functioning are one of the hallmarks of Huntington's disease (HD), a genetically caused neurodegenerative disorder. We applied functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to assess changes that occur with disease progression in the neural circuitry of key areas associated with executive and cognitive aspects of motor control. Seventy-seven healthy controls, 62 pre-symptomatic HD gene carriers (preHD), and 16 patients with manifest HD symptoms (earlyHD) performed a motor finger-tapping fMRI task with systematically varying speed and complexity. DCM was used to assess the causal interactions among seven pre-defined regions of interest, comprising primary motor cortex, supplementary motor area (SMA), dorsal premotor cortex, and superior parietal cortex. To capture heterogeneity among HD gene carriers, DCM parameters were entered into a hierarchical cluster analysis using Ward's method and squared Euclidian distance as a measure of similarity. After applying Bonferroni correction for the number of tests, DCM analysis revealed a group difference that was not present in the conventional fMRI analysis. We found an inhibitory effect of complexity on the connection from parietal to premotor areas in preHD, which became excitatory in earlyHD and correlated with putamen atrophy. While speed of finger movements did not modulate the connection from caudal to pre-SMA in controls and preHD, this connection became strongly negative in earlyHD. This second effect did not survive correction for multiple comparisons. Hierarchical clustering separated the gene mutation carriers into three clusters that also differed significantly between these two connections and thereby confirmed their relevance. DCM proved useful in identifying group differences that would have remained undetected by standard analyses and may aid in the investigation of between-subject heterogeneity. PMID:26635585
Calibrating the pixel-level Kepler imaging data with a causal data-driven model
NASA Astrophysics Data System (ADS)
Wang, Dun; Foreman-Mackey, Daniel; Hogg, David W.; Schölkopf, Bernhard
2015-01-01
In general, astronomical observations are affected by several kinds of noise, each with it's own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. In particular, the precision of NASA Kepler photometry for exoplanet science—the most precise photometric measurements of stars ever made—appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level (not the photometric measurement level); it can capture more fine-grained information about the variation of the spacecraft than is available in the pixel-summed aperture photometry. The basic idea is that CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits at the target star. In addition, we use the target star's future and past (auto-regression). By appropriately separating the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the fitting of the model. The method has four hyper-parameters (the number of predictor stars, the auto-regressive window size, and two L2-regularization amplitudes for model components), which we set by cross-validation. We determine a generic set of hyper-parameters that works well on most of the stars with 11≤V≤12 mag and apply the method to a corresponding set of target stars with known planet transits. We find that we can consistently outperform (for the purposes of exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for exoplanet discovery, often improving the SNR by a factor of two. While we have not yet exhaustively tested the method at other
ERIC Educational Resources Information Center
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
Hydrostatic equilibrium of causally consistent and dynamically stable neutron star models
NASA Astrophysics Data System (ADS)
Negi, P. S.
2008-08-01
We show that the mass-radius (M-R) relation corresponding to the stiffest equation of state (EOS) does not provide the necessary and sufficient condition of dynamical stability for equilibrium configurations, because such configurations cannot satisfy the `compatibility criterion'. In this regard, we construct sequences composed of core-envelope models such that, like the central condition belonging to the stiffest EOS, each member of these sequences satisfies the extreme case of the causality condition, v = c = 1, at the centre. We thereafter show that the M-R relation corresponding to the said core-envelope model sequences can provide the necessary and sufficient condition of dynamical stability only when the `compatibility criterion' for these sequences is `appropriately' satisfied. However, the `compatibility criterion' can remain satisfied even when the M-R relation does not provide the necessary and sufficient condition of dynamical stability for the equilibrium configurations. In continuation of the results of a previous study, these results explicitly show that the `compatibility criterion' independently provides, in general, the necessary and sufficient condition of hydrostatic equilibrium for any regular sequence. In addition to its fundamental result, this study can explain simultaneously the higher and the lower values of the glitch healing parameter observed for the Crab-like and Vela-like pulsars respectively, on the basis of the starquake model of glitch generation.
Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling
Cooray, Gerald K.; Sengupta, Biswa; Douglas, Pamela; Englund, Marita; Wickstrom, Ronny; Friston, Karl
2015-01-01
We characterised the pathophysiology of seizure onset in terms of slow fluctuations in synaptic efficacy using EEG in patients with anti-N-methyl-d-aspartate receptor (NMDA-R) encephalitis. EEG recordings were obtained from two female patients with anti-NMDA-R encephalitis with recurrent partial seizures (ages 19 and 31). Focal electrographic seizure activity was localised using an empirical Bayes beamformer. The spectral density of reconstructed source activity was then characterised with dynamic causal modelling (DCM). Eight models were compared for each patient, to evaluate the relative contribution of changes in intrinsic (excitatory and inhibitory) connectivity and endogenous afferent input. Bayesian model comparison established a role for changes in both excitatory and inhibitory connectivity during seizure activity (in addition to changes in the exogenous input). Seizures in both patients were associated with a sequence of changes in inhibitory and excitatory connectivity; a transient increase in inhibitory connectivity followed by a transient increase in excitatory connectivity and a final peak of excitatory–inhibitory balance at seizure offset. These systematic fluctuations in excitatory and inhibitory gain may be characteristic of (anti NMDA-R encephalitis) seizures. We present these results as a case study and replication to motivate analyses of larger patient cohorts, to see whether our findings generalise and further characterise the mechanisms of seizure activity in anti-NMDA-R encephalitis. PMID:26032883
Posttraumatic stress disorder among female Vietnam veterans: a causal model of etiology.
Fontana, A; Schwartz, L S; Rosenheck, R
1997-01-01
OBJECTIVES: The Vietnam and Persian Gulf wars have awakened people to the realization that military service can be traumatizing for women as well as men. This study investigated the etiological roles of both war and sexual trauma in the development of chronic posttraumatic stress disorder among female Vietnam veterans. METHODS: Data from the National Vietnam Veterans Readjustment Study for 396 Vietnam theater women and 250 Vietnam era women were analyzed with structural equation modeling. RESULTS: An etiological model with highly satisfactory fit and parsimony was developed. Exposure to war trauma contributed to the probability of posttraumatic stress disorder in theater women, as did sexual trauma in both theater and era women. Lack of social support at the time of homecoming acted as a powerful mediator of trauma for both groups of women. CONCLUSIONS: Within the constraints and assumptions of causal modeling, there is evidence that both war trauma and sexual trauma are powerful contributors to the development of posttraumatic stress disorder among female Vietnam veterans. PMID:9103092
Dynamic causal modeling of touch-evoked potentials in the rubber hand illusion.
Zeller, Daniel; Friston, Karl J; Classen, Joseph
2016-09-01
The neural substrate of bodily ownership can be disclosed by the rubber hand illusion (RHI); namely, the illusory self-attribution of an artificial hand that is induced by synchronous tactile stimulation of the subject's hand that is hidden from view. Previous studies have pointed to the premotor cortex (PMC) as a pivotal area in such illusions. To investigate the effective connectivity between - and within - sensory and premotor areas involved in bodily perceptions, we used dynamic causal modeling of touch-evoked responses in 13 healthy subjects. Each subject's right hand was stroked while viewing their own hand ("REAL"), or an artificial hand presented in an anatomically plausible ("CONGRUENT") or implausible ("INCONGRUENT") position. Bayesian model comparison revealed strong evidence for a differential involvement of the PMC in the generation of touch-evoked responses under the three conditions, confirming a crucial role of PMC in bodily self-attribution. In brief, the extrinsic (forward) connection from left occipital cortex to left PMC was stronger for CONGRUENT and INCONGRUENT as compared to REAL, reflecting the augmentation of bottom-up visual input when multisensory integration is challenged. Crucially, intrinsic connectivity in the primary somatosensory cortex (S1) was attenuated in the CONGRUENT condition, during the illusory percept. These findings support predictive coding models of the functional architecture of multisensory integration (and attenuation) in bodily perceptual experience. PMID:27241481
The development of causal categorization.
Hayes, Brett K; Rehder, Bob
2012-08-01
Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status, and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children's classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults. PMID:22462547
NASA Astrophysics Data System (ADS)
Everett, Allen E.
1996-06-01
Alcubierre recently exhibited a spacetime which, within the framework of general relativity, allows travel at superluminal speeds if matter with a negative energy density can exist, and conjectured that it should be possible to use similar techniques to construct a theory containing closed causal loops and, thus, travel backwards in time. We verify this conjecture by exhibiting a simple modification of Alcubierre's model, requiring no additional assumptions, in which causal loops are possible. We also note that this mechanism for generating causal loops differs in essential ways from that discovered by Gott involving cosmic strings.
Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating.
Cooray, Gerald K; Sengupta, Biswa; Douglas, Pamela K; Friston, Karl
2016-01-15
Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5-10min compared to approximately 1-2h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated. PMID:26220742
Dynamic causal modelling of electrographic seizure activity using Bayesian belief updating
Cooray, Gerald K.; Sengupta, Biswa; Douglas, Pamela K.; Friston, Karl
2016-01-01
Seizure activity in EEG recordings can persist for hours with seizure dynamics changing rapidly over time and space. To characterise the spatiotemporal evolution of seizure activity, large data sets often need to be analysed. Dynamic causal modelling (DCM) can be used to estimate the synaptic drivers of cortical dynamics during a seizure; however, the requisite (Bayesian) inversion procedure is computationally expensive. In this note, we describe a straightforward procedure, within the DCM framework, that provides efficient inversion of seizure activity measured with non-invasive and invasive physiological recordings; namely, EEG/ECoG. We describe the theoretical background behind a Bayesian belief updating scheme for DCM. The scheme is tested on simulated and empirical seizure activity (recorded both invasively and non-invasively) and compared with standard Bayesian inversion. We show that the Bayesian belief updating scheme provides similar estimates of time-varying synaptic parameters, compared to standard schemes, indicating no significant qualitative change in accuracy. The difference in variance explained was small (less than 5%). The updating method was substantially more efficient, taking approximately 5–10 min compared to approximately 1–2 h. Moreover, the setup of the model under the updating scheme allows for a clear specification of how neuronal variables fluctuate over separable timescales. This method now allows us to investigate the effect of fast (neuronal) activity on slow fluctuations in (synaptic) parameters, paving a way forward to understand how seizure activity is generated. PMID:26220742
ERIC Educational Resources Information Center
Witherington, David C.
2011-01-01
The dynamic systems (DS) approach has emerged as an influential and potentially unifying metatheory for developmental science. Its central platform--the argument against design--suggests that structure spontaneously and without prescription emerges through self-organization. In one of the most prominent accounts of DS, Thelen and her colleagues…
Modeling the dynamics of disaster evolution along causality networks with cycle chains
NASA Astrophysics Data System (ADS)
Li, Jian; Chen, Changkun
2014-05-01
A model for describing the evolution process of disasters, especially for disaster causality networks with cycle chains, has been developed. In the model, the impacts from the causative nodes, self-recovery behaviors, repair by government, internal noise and impacts outside the system have been taken into consideration. In particular, the cumulative effect of the inducing relationship between the causative node and its son node, due to cycle chain, has been quantified by the new model. Based on the proposed model, a parametric study, covering a range of conditional probability of directed inducing links, delay coefficient for disaster evolution and self-recovery coefficient during the recovery process, has been conducted by means of simulations. The results of these simulations point towards a phase transition of the disaster system with cycle chains when increasing conditional probability of directed inducing links or self-recovery coefficient. Particularly, we observe a critical conditional probability of directed inducing links and a critical self-recovery coefficient, beyond which, the whole system may be out of control after certain evolution time, regardless of the fact that the initial disturbance has disappeared. In addition, it is interesting to find that increasing delay coefficient cannot suppress the disaster evolution completely, for a disaster system that is potentially out of control due to the self-reinforce of cycle chains. Of course, the disaster evolution velocity drops when increasing delay coefficient, and this has a positive significance on disaster rescue. Further, it also illustrates that it is a bad strategy to arrange the total rescue resources uniformly during the disaster rescue process, while the strategy that disseminating more resources on the nodes in cycle chains and arranging the rescue resources in line with the potential maximum deviation of nodes, will have higher efficiency. With our model, it is possible for people to get an
Steinberg, Joel L.; Cunningham, Kathryn A.; Lane, Scott D.; Kramer, Larry A.; Narayana, Ponnada A.; Kosten, Thomas R.; Bechara, Antoine; Moeller, F. Gerard
2015-01-01
Abstract This study employed functional magnetic resonance imaging (fMRI)-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity underlying inhibitory behavioral control. fMRI data were acquired from 15 healthy subjects while they performed a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard NoGo conditions) in distinguishing spatial patterns of lines. Based on the previous inhibitory control literature and the present fMRI activation results, 10 brain regions were postulated as nodes in the effective connectivity model. Due to the large number of potential interconnections among these nodes, the number of models for final analysis was reduced to a manageable level for the whole group by conducting DCM Network Discovery, which is a recently developed option within the Statistical Parametric Mapping software package. Given the optimum network model, the DCM Network Discovery analysis found that the locations of the driving input into the model from all the experimental stimuli in the Go/NoGo task were the amygdala and the hippocampus. The strengths of several cortico-subcortical connections were modulated (influenced) by the two NoGo conditions. Specifically, connectivity from the middle frontal gyrus (MFG) to hippocampus was enhanced by the Easy condition and further enhanced by the Hard NoGo condition, possibly suggesting that compared with the Easy NoGo condition, stronger control from MFG was needed for the hippocampus to discriminate/learn the spatial pattern in order to respond correctly (inhibit), during the Hard NoGo condition. PMID:25336321
Ma, Liangsuo; Steinberg, Joel L; Cunningham, Kathryn A; Lane, Scott D; Kramer, Larry A; Narayana, Ponnada A; Kosten, Thomas R; Bechara, Antoine; Moeller, F Gerard
2015-04-01
This study employed functional magnetic resonance imaging (fMRI)-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity underlying inhibitory behavioral control. fMRI data were acquired from 15 healthy subjects while they performed a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard NoGo conditions) in distinguishing spatial patterns of lines. Based on the previous inhibitory control literature and the present fMRI activation results, 10 brain regions were postulated as nodes in the effective connectivity model. Due to the large number of potential interconnections among these nodes, the number of models for final analysis was reduced to a manageable level for the whole group by conducting DCM Network Discovery, which is a recently developed option within the Statistical Parametric Mapping software package. Given the optimum network model, the DCM Network Discovery analysis found that the locations of the driving input into the model from all the experimental stimuli in the Go/NoGo task were the amygdala and the hippocampus. The strengths of several cortico-subcortical connections were modulated (influenced) by the two NoGo conditions. Specifically, connectivity from the middle frontal gyrus (MFG) to hippocampus was enhanced by the Easy condition and further enhanced by the Hard NoGo condition, possibly suggesting that compared with the Easy NoGo condition, stronger control from MFG was needed for the hippocampus to discriminate/learn the spatial pattern in order to respond correctly (inhibit), during the Hard NoGo condition. PMID:25336321
The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding.
Fogelson, Noa; Litvak, Vladimir; Peled, Avi; Fernandez-del-Olmo, Miguel; Friston, Karl
2014-09-01
This paper tests the hypothesis that patients with schizophrenia have a deficit in selectively attending to predictable events. We used dynamic causal modeling (DCM) of electrophysiological responses - to predictable and unpredictable visual targets - to quantify the effective connectivity within and between cortical sources in the visual hierarchy in 25 schizophrenia patients and 25 age-matched controls. We found evidence for marked differences between normal subjects and schizophrenia patients in the strength of extrinsic backward connections from higher hierarchical levels to lower levels within the visual system. In addition, we show that not only do schizophrenia subjects have abnormal connectivity but also that they fail to adjust or optimize this connectivity when events can be predicted. Thus, the differential intrinsic recurrent connectivity observed during processing of predictable versus unpredictable targets was markedly attenuated in schizophrenia patients compared with controls, suggesting a failure to modulate the sensitivity of neurons responsible for passing sensory information of prediction errors up the visual cortical hierarchy. The findings support the proposed role of abnormal connectivity in the neuropathology and pathophysiology of schizophrenia. PMID:24998031
NASA Astrophysics Data System (ADS)
Valente, Giovanni; Owen Weatherall, James
2014-11-01
Relativity theory is often taken to include, or to imply, a prohibition on superluminal propagation of causal processes. Yet, what exactly the prohibition on superluminal propagation amounts to and how one should deal with its possible violation have remained open philosophical problems, both in the context of the metaphysics of causation and the foundations of physics. In particular, recent work in philosophy of physics has focused on the causal structure of spacetime in relativity theory and on how this causal structure manifests itself in our most fundamental theories of matter. These topics were the subject of a workshop on "Relativistic Causality in Quantum Field Theory and General Relativity" that we organized (along with John Earman) at the Center for Philosophy of Science in Pittsburgh on April 5-7, 2013. The present Special Issue comprises contributions by speakers in that workshop as well as several other experts exploring different aspects of relativistic causality. We are grateful to the journal for hosting this Special Issue, to the journal's managing editor, Femke Kuiling, for her help and support in putting the issue together, and to the authors and the referees for their excellent work.
From Granger causality to long-term causality: Application to climatic data
NASA Astrophysics Data System (ADS)
Smirnov, Dmitry A.; Mokhov, Igor I.
2009-07-01
Quantitative characterization of interaction between processes from time series is often required in different fields of natural science including geophysics and biophysics. Typically, one estimates “short-term” influences, e.g., the widely used Granger causality is defined via one-step-ahead predictions. Such an approach does not reveal how strongly the “long-term” behavior of one process under study is affected by the others. To overcome this problem, we introduce the concept of long-term causality, which extends the concept of Granger causality. The long-term causality is estimated from data via empirical modeling and analysis of model dynamics under different conditions. Apart from mathematical examples, we apply both approaches to find out how strongly the global surface temperature (GST) is affected by variations in carbon dioxide atmospheric content, solar activity, and volcanic activity during the last 150 years. Influences of all the three factors on GST are detected with the Granger causality. However, the long-term causality shows that the rise in GST during the last decades can be explained only if the anthropogenic factor (CO2) is taken into account in a model.
Chen, Mei-Chih; Chang, Kaowen
2014-01-01
Many city governments choose to supply more developable land and transportation infrastructure with the hope of attracting people and businesses to their cities. However, like those in Taiwan, major cities worldwide suffer from traffic congestion. This study applies the system thinking logic of the causal loops diagram (CLD) model in the System Dynamics (SD) approach to analyze the issue of traffic congestion and other issues related to roads and land development in Taiwan’s cities. Comparing the characteristics of development trends with yearbook data for 2002 to 2013 for all of Taiwan’s cities, this study explores the developing phenomenon of unlimited city sprawl and identifies the cause and effect relationships in the characteristics of development trends in traffic congestion, high-density population aggregation in cities, land development, and green land disappearance resulting from city sprawl. This study provides conclusions for Taiwan’s cities’ sustainability and development (S&D). When developing S&D policies, during decision making processes concerning city planning and land use management, governments should think with a holistic view of carrying capacity with the assistance of system thinking to clarify the prejudices in favor of the unlimited developing phenomena resulting from city sprawl. PMID:25383609
Koscik, Timothy R.; Tranel, Daniel
2013-01-01
People tend to assume that outcomes are caused by dispositional factors, e.g., a person’s constitution or personality, even when the actual cause is due to situational factors, e.g., luck or coincidence. This is known as the ‘correspondence bias.’ This tendency can lead normal, intelligent persons to make suboptimal decisions. Here, we used a neuropsychological approach to investigate the neural basis of the correspondence bias, by studying economic decision-making in patients with damage to the ventromedial prefrontal cortex (vmPFC). Given the role of the vmPFC in social cognition, we predicted that vmPFC is necessary for the normal correspondence bias. In our experiment, consistent with expectations, healthy (N=46) and brain-damaged (N=30) comparison participants displayed the correspondence bias when investing and invested no differently when given dispositional or situational information. By contrast, vmPFC patients (N=17) displayed a lack of correspondence bias and invested more when given dispositional than situational information. The results support the conclusion that vmPFC is critical for normal social inference and the correspondence bias, and our findings help clarify the important (and potentially disadvantageous) role of social inference in economic decision-making. PMID:23574584
Noreika, Valdas; Gueorguiev, David; Shtyrov, Yury; Bekinschtein, Tristan A.; Henson, Richard
2016-01-01
There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called “mismatch response”). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an “omission” response). This situation arguably provides a more direct measure of “top-down” predictions in the absence of confounding “bottom-up” input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of “bottom-up” stimuli with the presence versus absence of “top-down” attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward “prediction” connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction. SIGNIFICANCE STATEMENT Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known “mismatch response.” But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain
Redundant variables and Granger causality
NASA Astrophysics Data System (ADS)
Angelini, L.; de Tommaso, M.; Marinazzo, D.; Nitti, L.; Pellicoro, M.; Stramaglia, S.
2010-03-01
We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.
Beaumelle, Léa; Vile, Denis; Lamy, Isabelle; Vandenbulcke, Franck; Gimbert, Frédéric; Hedde, Mickaël
2016-11-01
Structural equation models (SEM) are increasingly used in ecology as multivariate analysis that can represent theoretical variables and address complex sets of hypotheses. Here we demonstrate the interest of SEM in ecotoxicology, more precisely to test the three-step concept of metal bioavailability to earthworms. The SEM modeled the three-step causal chain between environmental availability, environmental bioavailability and toxicological bioavailability. In the model, each step is an unmeasured (latent) variable reflected by several observed variables. In an exposure experiment designed specifically to test this SEM for Cd, Pb and Zn, Aporrectodea caliginosa was exposed to 31 agricultural field-contaminated soils. Chemical and biological measurements used included CaC12-extractable metal concentrations in soils, free ion concentration in soil solution as predicted by a geochemical model, dissolved metal concentration as predicted by a semi-mechanistic model, internal metal concentrations in total earthworms and in subcellular fractions, and several biomarkers. The observations verified the causal definition of Cd and Pb bioavailability in the SEM, but not for Zn. Several indicators consistently reflected the hypothetical causal definition and could thus be pertinent measurements of Cd and Pb bioavailability to earthworm in field-contaminated soils. SEM highlights that the metals present in the soil solution and easily extractable are not the main source of available metals for earthworms. This study further highlights SEM as a powerful tool that can handle natural ecosystem complexity, thus participating to the paradigm change in ecotoxicology from a bottom-up to a top-down approach. PMID:27378153
Lusk, S L; Ronis, D L; Hogan, M M
1997-06-01
The health promotion model (HPM) was tested as a causal model of construction workers' use of hearing protection (N = 359). Theoretical and exploratory models fit well, with the theoretical model accounting for 36.3% of variance and the exploratory model accounting for 50.6% of variance in hearing protection use. Value of use (benefits of using hearing protection), barriers to use, and self-efficacy were significant predictors in both the theoretical and exploratory models, but perceived health status was a predictor only in the theoretical model. In the exploratory model, where modifying factors were allowed direct relationships with use of hearing protection, two modifying factors--noise exposure and interpersonal influences-modeling--were significant predictors. Results of this test of the HPM are consistent with the revised HPM (Pender, 1996). There were significant direct paths from modifying factors to behaviour. Use of hearing protection was best predicted by behavior-specific predictors, such as perceived barriers to use of hearing protection. Results support the use of the HPM to predict use of hearing protection. PMID:9179173
Aging into Perceptual Control: A Dynamic Causal Modeling for fMRI Study of Bistable Perception
Dowlati, Ehsan; Adams, Sarah E.; Stiles, Alexandra B.; Moran, Rosalyn J.
2016-01-01
Aging is accompanied by stereotyped changes in functional brain activations, for example a cortical shift in activity patterns from posterior to anterior regions is one hallmark revealed by functional magnetic resonance imaging (fMRI) of aging cognition. Whether these neuronal effects of aging could potentially contribute to an amelioration of or resistance to the cognitive symptoms associated with psychopathology remains to be explored. We used a visual illusion paradigm to address whether aging affects the cortical control of perceptual beliefs and biases. Our aim was to understand the effective connectivity associated with volitional control of ambiguous visual stimuli and to test whether greater top-down control of early visual networks emerged with advancing age. Using a bias training paradigm for ambiguous images we found that older participants (n = 16) resisted experimenter-induced visual bias compared to a younger cohort (n = 14) and that this resistance was associated with greater activity in prefrontal and temporal cortices. By applying Dynamic Causal Models for fMRI we uncovered a selective recruitment of top-down connections from the middle temporal to Lingual gyrus (LIN) by the older cohort during the perceptual switch decision following bias training. In contrast, our younger cohort did not exhibit any consistent connectivity effects but instead showed a loss of driving inputs to orbitofrontal sources following training. These findings suggest that perceptual beliefs are more readily controlled by top-down strategies in older adults and introduce age-dependent neural mechanisms that may be important for understanding aberrant belief states associated with psychopathology. PMID:27064235
Albouy, Philippe; Mattout, Jérémie; Sanchez, Gaëtan; Tillmann, Barbara; Caclin, Anne
2015-01-01
Congenital amusia is a neuro-developmental disorder that primarily manifests as a difficulty in the perception and memory of pitch-based materials, including music. Recent findings have shown that the amusic brain exhibits altered functioning of a fronto-temporal network during pitch perception and short-term memory. Within this network, during the encoding of melodies, a decreased right backward frontal-to-temporal connectivity was reported in amusia, along with an abnormal connectivity within and between auditory cortices. The present study investigated whether connectivity patterns between these regions were affected during the short-term memory retrieval of melodies. Amusics and controls had to indicate whether sequences of six tones that were presented in pairs were the same or different. When melodies were different only one tone changed in the second melody. Brain responses to the changed tone in “Different” trials and to its equivalent (original) tone in “Same” trials were compared between groups using Dynamic Causal Modeling (DCM). DCM results confirmed that congenital amusia is characterized by an altered effective connectivity within and between the two auditory cortices during sound processing. Furthermore, right temporal-to-frontal message passing was altered in comparison to controls, with notably an increase in “Same” trials. An additional analysis in control participants emphasized that the detection of an unexpected event in the typically functioning brain is supported by right fronto-temporal connections. The results can be interpreted in a predictive coding framework as reflecting an abnormal prediction error sent by temporal auditory regions towards frontal areas in the amusic brain. PMID:25698955
Ma, Liangsuo; Steinberg, Joel L.; Cunningham, Kathryn A.; Lane, Scott D.; Bjork, James M.; Neelakantan, Harshini; Price, Amanda E.; Narayana, Ponnada A.; Kosten, Thomas R.; Bechara, Antoine; Moeller, F. Gerard
2015-01-01
Cocaine dependence is associated with increased impulsivity in humans. Both cocaine dependence and impulsive behavior are under the regulatory control of cortico-striatal networks. One behavioral laboratory measure of impulsivity is response inhibition (ability to withhold a prepotent response) in which altered patterns of regional brain activation during executive tasks in service of normal performance are frequently found in cocaine dependent (CD) subjects studied with functional magnetic resonance imaging (fMRI). However, little is known about aberrations in specific directional neuronal connectivity in CD subjects. The present study employed fMRI-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity associated with response inhibition in CD subjects, elicited under performance of a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard). The performance on the Go/NoGo task was not significantly different between CD subjects and controls. The DCM analysis revealed that prefrontal–striatal connectivity was modulated (influenced) during the NoGo conditions for both groups. The effective connectivity from left (L) anterior cingulate cortex (ACC) to L caudate was similarly modulated during the Easy NoGo condition for both groups. During the Hard NoGo condition in controls, the effective connectivity from right (R) dorsolateral prefrontal cortex (DLPFC) to L caudate became more positive, and the effective connectivity from R ventrolateral prefrontal cortex (VLPFC) to L caudate became more negative. In CD subjects, the effective connectivity from L ACC to L caudate became more negative during the Hard NoGo conditions. These results indicate that during Hard NoGo trials in CD subjects, the ACC rather than DLPFC or VLPFC influenced caudate during response inhibition. PMID:26082893
Ma, Liangsuo; Steinberg, Joel L; Cunningham, Kathryn A; Lane, Scott D; Bjork, James M; Neelakantan, Harshini; Price, Amanda E; Narayana, Ponnada A; Kosten, Thomas R; Bechara, Antoine; Moeller, F Gerard
2015-01-01
Cocaine dependence is associated with increased impulsivity in humans. Both cocaine dependence and impulsive behavior are under the regulatory control of cortico-striatal networks. One behavioral laboratory measure of impulsivity is response inhibition (ability to withhold a prepotent response) in which altered patterns of regional brain activation during executive tasks in service of normal performance are frequently found in cocaine dependent (CD) subjects studied with functional magnetic resonance imaging (fMRI). However, little is known about aberrations in specific directional neuronal connectivity in CD subjects. The present study employed fMRI-based dynamic causal modeling (DCM) to study the effective (directional) neuronal connectivity associated with response inhibition in CD subjects, elicited under performance of a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard). The performance on the Go/NoGo task was not significantly different between CD subjects and controls. The DCM analysis revealed that prefrontal-striatal connectivity was modulated (influenced) during the NoGo conditions for both groups. The effective connectivity from left (L) anterior cingulate cortex (ACC) to L caudate was similarly modulated during the Easy NoGo condition for both groups. During the Hard NoGo condition in controls, the effective connectivity from right (R) dorsolateral prefrontal cortex (DLPFC) to L caudate became more positive, and the effective connectivity from R ventrolateral prefrontal cortex (VLPFC) to L caudate became more negative. In CD subjects, the effective connectivity from L ACC to L caudate became more negative during the Hard NoGo conditions. These results indicate that during Hard NoGo trials in CD subjects, the ACC rather than DLPFC or VLPFC influenced caudate during response inhibition. PMID:26082893
Albouy, Philippe; Mattout, Jérémie; Sanchez, Gaëtan; Tillmann, Barbara; Caclin, Anne
2015-01-01
Congenital amusia is a neuro-developmental disorder that primarily manifests as a difficulty in the perception and memory of pitch-based materials, including music. Recent findings have shown that the amusic brain exhibits altered functioning of a fronto-temporal network during pitch perception and short-term memory. Within this network, during the encoding of melodies, a decreased right backward frontal-to-temporal connectivity was reported in amusia, along with an abnormal connectivity within and between auditory cortices. The present study investigated whether connectivity patterns between these regions were affected during the short-term memory retrieval of melodies. Amusics and controls had to indicate whether sequences of six tones that were presented in pairs were the same or different. When melodies were different only one tone changed in the second melody. Brain responses to the changed tone in "Different" trials and to its equivalent (original) tone in "Same" trials were compared between groups using Dynamic Causal Modeling (DCM). DCM results confirmed that congenital amusia is characterized by an altered effective connectivity within and between the two auditory cortices during sound processing. Furthermore, right temporal-to-frontal message passing was altered in comparison to controls, with notably an increase in "Same" trials. An additional analysis in control participants emphasized that the detection of an unexpected event in the typically functioning brain is supported by right fronto-temporal connections. The results can be interpreted in a predictive coding framework as reflecting an abnormal prediction error sent by temporal auditory regions towards frontal areas in the amusic brain. PMID:25698955
Experimental test of nonlocal causality.
Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G; Fedrizzi, Alessandro
2016-08-01
Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045
Experimental test of nonlocal causality
Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G.; Fedrizzi, Alessandro
2016-01-01
Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell’s local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045
Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models
Daunizeau, J.; Friston, K.J.; Kiebel, S.J.
2009-01-01
In this paper, we describe a general variational Bayesian approach for approximate inference on nonlinear stochastic dynamic models. This scheme extends established approximate inference on hidden-states to cover: (i) nonlinear evolution and observation functions, (ii) unknown parameters and (precision) hyperparameters and (iii) model comparison and prediction under uncertainty. Model identification or inversion entails the estimation of the marginal likelihood or evidence of a model. This difficult integration problem can be finessed by optimising a free-energy bound on the evidence using results from variational calculus. This yields a deterministic update scheme that optimises an approximation to the posterior density on the unknown model variables. We derive such a variational Bayesian scheme in the context of nonlinear stochastic dynamic hierarchical models, for both model identification and time-series prediction. The computational complexity of the scheme is comparable to that of an extended Kalman filter, which is critical when inverting high dimensional models or long time-series. Using Monte-Carlo simulations, we assess the estimation efficiency of this variational Bayesian approach using three stochastic variants of chaotic dynamic systems. We also demonstrate the model comparison capabilities of the method, its self-consistency and its predictive power. PMID:19862351
Leslie, Hannah H.; Karasek, Deborah A.; Harris, Laura F.; Chang, Emily; Abdulrahim, Naila; Maloba, May; Huchko, Megan J.
2014-01-01
Objective To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. Background Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. Methods We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. Results We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. Conclusion Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased
NASA Astrophysics Data System (ADS)
Bialas, Piotr
2003-10-01
We discuss the geometry of trees endowed with a causal structure using the conventional framework of equilibrium statistical mechanics. We show how this ensemble is related to popular growing network models. In particular we demonstrate that on a class of afine attachment kernels the two models are identical but they can differ substantially for other choice of weights. We show that causal trees exhibit condensation even for asymptotically linear kernels. We derive general formulae describing the degree distribution, the ancestor--descendant correlation and the probability that a randomly chosen node lives at a given geodesic distance from the root. It is shown that the Hausdorff dimension dH of the causal networks is generically infinite.
Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Tu, Tao; Kochlka, John; Cai, Weidong; Menon, Vinod
2016-01-01
Background Causal estimation methods are increasingly being used to investigate functional brain networks in fMRI, but there are continuing concerns about the validity of these methods. New Method Multivariate Dynamical Systems (MDS) is a state-space method for estimating dynamic causal interactions in fMRI data. Here we validate MDS using benchmark simulations as well as simulations from a more realistic stochastic neurophysiological model. Finally, we applied MDS to investigate dynamic casual interactions in a fronto-cingulate-parietal control network using Human Connectome Project (HCP) data acquired during performance of a working memory task. Crucially, since the ground truth in experimental data is unknown, we conducted novel stability analysis to determine robust causal interactions within this network. Results MDS accurately recovered dynamic causal interactions with an area under receiver operating characteristic (AUC) above 0.7 for benchmark datasets and AUC above 0.9 for datasets generated using the neurophysiological model. In experimental fMRI data, bootstrap procedures revealed a stable pattern of causal influences from the anterior insula to other nodes of the fronto-cingulate-parietal network. Comparison with Existing Methods MDS is effective in estimating dynamic causal interactions in both the benchmark and neurophysiological model based datasets in terms of AUC, sensitivity and false positive rates. Conclusions Our findings demonstrate that MDS can accurately estimate causal interactions in fMRI data. Neurophysiological models and stability analysis provide a general framework for validating computational methods designed to estimate causal interactions in fMRI. The right anterior insula functions as a causal hub during working memory. PMID:27015792
A New Life-Span Approach to Conscientiousness and Health: Combining the Pieces of the Causal Puzzle
ERIC Educational Resources Information Center
Friedman, Howard S.; Kern, Margaret L.; Hampson, Sarah E.; Duckworth, Angela Lee
2014-01-01
Conscientiousness has been shown to predict healthy behaviors, healthy social relationships, and physical health and longevity. The causal links, however, are complex and not well elaborated. Many extant studies have used comparable measures for conscientiousness, and a systematic endeavor to build cross-study analyses for conscientiousness and…
ERIC Educational Resources Information Center
Levine, Judith A.; Pollack, Harold
This study used linked maternal-child data from the 1997-1998 National Longitudinal Survey of Youth to explore the wellbeing of children born to teenage mothers. Two econometric techniques explored the causal impact of early childbearing on subsequent child and adolescent outcomes. First, a fixed-effect, cousin-comparison analysis controlled for…
Structural Equations and Causal Explanations: Some Challenges for Causal SEM
ERIC Educational Resources Information Center
Markus, Keith A.
2010-01-01
One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…
Xu, Haojie; Lu, Yunfeng; Zhu, Shanan
2014-01-01
It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The non-zero covariance of the model’s residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In the present study, we firstly investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in
Identification of causal genes for complex traits
Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun; Pasaniuc, Bogdan; Eskin, Eleazar
2015-01-01
Motivation: Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider ‘causal variants’ as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations. Results: In this work, we propose CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of our approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability ρ. Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches. We validate our method using a real mouse high-density lipoprotein data (HDL) and show that CAVIAR-Gene is able to identify Apoa2 (a gene known to harbor causal variants for HDL), while reducing the number of genes that need to be tested for functionality by a factor of 2. Availability and implementation: Software is freely available for download at genetics.cs.ucla.edu/caviar. Contact: eeskin@cs.ucla.edu PMID:26072484
Jennings, Wesley G; Park, MiRang; Richards, Tara N; Tomsich, Elizabeth; Gover, Angela; Powers, Ráchael A
2014-12-01
Child maltreatment is one of the most commonly examined risk factors for violence in dating relationships. Often referred to as the intergenerational transmission of violence or cycle of violence, a fair amount of research suggests that experiencing abuse during childhood significantly increases the likelihood of involvement in violent relationships later, but these conclusions are primarily based on correlational research designs. Furthermore, the majority of research linking childhood maltreatment and dating violence has focused on samples of young people from the United States. Considering these limitations, the current study uses a rigorous, propensity score matching approach to estimate the causal effect of experiencing child physical abuse on adult dating violence among a large sample of South Korean emerging adults. Results indicate that the link between child physical abuse and adult dating violence is spurious rather than causal. Study limitations and implications are discussed. PMID:25220481
ERIC Educational Resources Information Center
Kaufmann, Stefan
2013-01-01
The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal…
Confounding effects of indirect connections on causality estimation.
Vakorin, Vasily A; Krakovska, Olga A; McIntosh, Anthony R
2009-10-30
Addressing the issue of effective connectivity, this study focuses on effects of indirect connections on inferring stable causal relations: partial transfer entropy. We introduce a Granger causality measure based on a multivariate version of transfer entropy. The statistic takes into account the influence of the rest of the network (environment) on observed coupling between two given nodes. This formalism allows us to quantify, for a specific pathway, the total amount of indirect coupling mediated by the environment. We show that partial transfer entropy is a more sensitive technique to identify robust causal relations than its bivariate equivalent. In addition, we demonstrate the confounding effects of the variation in indirect coupling on the detectability of robust causal links. Finally, we consider the problem of model misspecification and its effect on the robustness of the observed connectivity patterns, showing that misspecifying the model may be an issue even for model-free information-theoretic approach. PMID:19628006
Causal Inference in Retrospective Studies.
ERIC Educational Resources Information Center
Holland, Paul W.; Rubin, Donald B.
1988-01-01
The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…
Zigzagging causality EPR model: answer to Vigier and coworkers and to Sutherland
de Beauregard, O.C.
1987-08-01
The concept of propagation in time of Vigier and co-workers (V et al.) implies the ideal of a supertime; it is thus alien to most Minkowskian pictures and certainly to the authors. From this stems much of V et al.'s misunderstandings of his position. In steady motion of a classical fluid nobody thinks that momentum conservation is violated, or that momentum is shot upstream without cause because of the suction from the sinks. Similarly with momentum-energy in spacetime and the acceptance of an advanced causality. As for the CT invariance of the Feynman propagator, the causality asymmetry it entails is factlike, not lawlike. The geometrical counterpart of the symmetry between prediction and retrodiction and between retarded and advanced waves, as expressed in the alternative expressions
ERIC Educational Resources Information Center
Karabatsos, G.; Walker, S.G.
2010-01-01
Causal inference is central to educational research, where in data analysis the aim is to learn the causal effects of educational treatments on academic achievement, to evaluate educational policies and practice. Compared to a correlational analysis, a causal analysis enables policymakers to make more meaningful statements about the efficacy of…
The Agent-based Approach: A New Direction for Computational Models of Development.
ERIC Educational Resources Information Center
Schlesinger, Matthew; Parisi, Domenico
2001-01-01
Introduces the concepts of online and offline sampling and highlights the role of online sampling in agent-based models of learning and development. Compares the strengths of each approach for modeling particular developmental phenomena and research questions. Describes a recent agent-based model of infant causal perception. Discusses limitations…
From meta-omics to causality: experimental models for human microbiome research.
Fritz, Joëlle V; Desai, Mahesh S; Shah, Pranjul; Schneider, Jochen G; Wilmes, Paul
2013-01-01
Large-scale 'meta-omic' projects are greatly advancing our knowledge of the human microbiome and its specific role in governing health and disease states. A myriad of ongoing studies aim at identifying links between microbial community disequilibria (dysbiosis) and human diseases. However, due to the inherent complexity and heterogeneity of the human microbiome, cross-sectional, case-control and longitudinal studies may not have enough statistical power to allow causation to be deduced from patterns of association between variables in high-resolution omic datasets. Therefore, to move beyond reliance on the empirical method, experiments are critical. For these, robust experimental models are required that allow the systematic manipulation of variables to test the multitude of hypotheses, which arise from high-throughput molecular studies. Particularly promising in this respect are microfluidics-based in vitro co-culture systems, which allow high-throughput first-pass experiments aimed at proving cause-and-effect relationships prior to testing of hypotheses in animal models. This review focuses on widely used in vivo, in vitro, ex vivo and in silico approaches to study host-microbial community interactions. Such systems, either used in isolation or in a combinatory experimental approach, will allow systematic investigations of the impact of microbes on the health and disease of the human host. All the currently available models present pros and cons, which are described and discussed. Moreover, suggestions are made on how to develop future experimental models that not only allow the study of host-microbiota interactions but are also amenable to high-throughput experimentation. PMID:24450613
Modelling approaches for angiogenesis.
Taraboletti, G; Giavazzi, R
2004-04-01
The development of a functional vasculature within a tumour is a requisite for its growth and progression. This fact has led to the design of therapies directed toward the tumour vasculature, aiming either to prevent the formation of new vessels (anti-angiogenic) or to damage existing vessels (vascular targeting). The development of agents with different mechanisms of action requires powerful preclinical models for the analysis and optimization of these therapies. This review concerns 'classical' assays of angiogenesis in vitro and in vivo, recent approaches to target identification (analysis of gene and protein expression), and the study of morphological and functional changes in the vasculature in vivo (imaging techniques). It mainly describes assays designed for anti-angiogenic compounds, indicating, where possible, their application to the study of vascular-targeting agents. PMID:15120043
Paradoxical Behavior of Granger Causality
NASA Astrophysics Data System (ADS)
Witt, Annette; Battaglia, Demian; Gail, Alexander
2013-03-01
Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen
Analyzing multiple spike trains with nonparametric Granger causality.
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou
2009-08-01
Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation. PMID:19137420
Zelko, Hilary; Zammar, Guilherme Roberto; Bonilauri Ferreira, Ana Paula; Phadtare, Amruta; Shah, Jatin; Pietrobon, Ricardo
2010-01-01
Background Although scientific innovation has been a long-standing topic of interest for historians, philosophers and cognitive scientists, few studies in biomedical research have examined from researchers' perspectives how high impact publications are developed and why they are consistently produced by a small group of researchers. Our objective was therefore to interview a group of researchers with a track record of high impact publications to explore what mechanism they believe contribute to the generation of high impact publications. Methodology/Principal Findings Researchers were located in universities all over the globe and interviews were conducted by phone. All interviews were transcribed using standard qualitative methods. A Grounded Theory approach was used to code each transcript, later aggregating concept and categories into overarching explanation model. The model was then translated into a System Dynamics mathematical model to represent its structure and behavior. Five emerging themes were found in our study. First, researchers used heuristics or rules of thumb that came naturally to them. Second, these heuristics were reinforced by positive feedback from their peers and mentors. Third, good communication skills allowed researchers to provide feedback to their peers, thus closing a positive feedback loop. Fourth, researchers exhibited a number of psychological attributes such as curiosity or open-mindedness that constantly motivated them, even when faced with discouraging situations. Fifth, the system is dominated by randomness and serendipity and is far from a linear and predictable environment. Some researchers, however, took advantage of this randomness by incorporating mechanisms that would allow them to benefit from random findings. The aggregation of these themes into a policy model represented the overall expected behavior of publications and their impact achieved by high impact researchers. Conclusions The proposed selection mechanism
[Psychological stress factors in erectile dysfunctions. Causal models and empirical results].
Hartmann, U
1998-09-01
In this paper the role of psychosocial factors in erectile dysfunction is examined in two different ways: (1) Current approaches to the causation of psychogenic erectile dysfunctions are reviewed and discussed. (2) Empirical results from a large unselected sample of sexually dysfunctional men are presented and compared to a sample of functional men. Concerning etiological models the traditional unidimensional dichotomous concepts (psychogenic versus organic) of erectile dysfunction have to be abandoned and replaced by two-dimensional models that are able to take the clinical reality into account that many patients have both significant psychological and organic factors in their disorder. The main causes of psychogenic erectile disorders can be divided into three groups, each belonging to a different phase of time: (i) immediate factors (performance anxiety), (ii) antecedent life events from recent history, (iii) developmental vulnerabilities from childhood and adolescence. The specific interplay as well as the importance of the different groups is different in primary and secondary erectile disorders. The empirical results presented here are based on a sample of 751 patients from our interdisciplinary outpatient unit for sexually dysfunctional men and a group of 55 sexually functional men. Both groups completed a self-developed, multidimensional questionnaire addressing a variety of psychosocial and descriptive factors concerning erectile disorders. The results prove the heterogeneity of patients and their respective erectile problems and show a number of highly significant group differences. The frequent comorbidity of erectile disorders and premature ejaculation and disorders of desire is worth mentioning as well as the high prevalence of depression and the extreme extent of performance anxiety in the patient group. The results are discussed with respect to future treatment strategies. The necessity of combined psychosomatic approaches optimizing the efficacy of
Multidimensional causal model of dental caries development in low-income preschool children.
Litt, M D; Reisine, S; Tinanoff, N
1995-01-01
Despite the decline in the incidence of dental caries in the United States over the past several years, the condition remains a significant problem for the nation's poor children. Efforts to identify the factors responsible for caries development in samples of children of low socioeconomic status have primarily focused on a limited number of variables, and those have been predominantly biological (mutans streptococci, for example). Resulting models of caries development have usually shown good sensitivity but poor specificity. They have had limited implications for treatment. In an effort to produce a comprehensive model of caries development, 184 low-income preschool children were clinically assessed for mutans streptococci and for decayed, missing, or filled surfaces of deciduous teeth twice, first at age 4 years (baseline) and again a year later (year 1 assessment). As the clinical assessments were being done, caretakers were being interviewed to obtain data from five domains: demographics, social status, dental health behaviors, cognitive factors such as self-efficacy (self-confidence) and controllability, and perceived life stress. Data were analyzed using a structural equations modeling approach in which variables from all domains, plus baseline decayed missing and filled surfaces and baseline mutants, were used together to create a model of caries development in the year 1 assessment. Results confirmed earlier work that suggested that caries development at a 1-year followup was strongly dependent on earlier caries development. Early caries development in this sample was determined in part by mutans levels and by dental health behaviors. These behaviors themselves were accounted for partly by a cognitive factor.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7480616
Representing Personal Determinants in Causal Structures.
ERIC Educational Resources Information Center
Bandura, Albert
1984-01-01
Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…
Expectations and Interpretations during Causal Learning
ERIC Educational Resources Information Center
Luhmann, Christian C.; Ahn, Woo-kyoung
2011-01-01
In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to…
Re-assessing causal accounts of learnt behavior in rats.
Burgess, K V; Dwyer, D M; Honey, R C
2012-04-01
Rats received either a common-cause (i.e., A→B, A→food) or a causal-chain training scenario (i.e., B→A, A→food) before their tendency to approach the food magazine during the presentation of B was assessed as a function of whether it was preceded by a potential alternative cause. Causal model theory predicts that the influence of an alternative cause should be restricted to the common-cause scenario. In Experiment 1, responding to B was reduced when it occurred after pressing a novel lever during the test phase. This effect was not influenced by the type of training scenario. In Experiment 2, rats were familiarized with the lever prior to test by training it as a potential cause of B. After this treatment, the lever now failed to influence test responding to B. In Experiment 3, rats given common-cause training responded more to B when it followed a cue that had previously been trained as a predictor of B, than when it followed another stimulus. This effect was not apparent in rats that received causal-chain training. This pattern of results is the opposite of that predicted by causal model theory. Thus, in three experiments, the presence of an alternative cause failed to influence test responding in manner consistent with causal model theory. These results undermine the application of causal model theory to rats, but are consistent with associative analyses. PMID:22486754
ERIC Educational Resources Information Center
Truman, G. E.
2009-01-01
Behaviour modelling has been associated with higher learning outcomes compared to other training approaches. These cumulative research findings create imperative to examine underlying causal mechanisms or contingency factors that may promote behaviour modelling's advantages even further. We propose group-based learning as one contingency factor…
NASA Astrophysics Data System (ADS)
Haase, D.
2009-04-01
Participation processes play a crucial role in implementing adaptive management in river basins. A range of different participative methods is being applied, however, little is known on their effectiveness in addressing the specific question or policy process at stake and their performance in different socio-economic and cultural settings. To shed light on the role of cultural settings on the outcomes of a participative process we carried out a comparative study of participation processes using group model building (GMB) in a European, a Central Asian, and an African river basin. We use an analytical framework which covers the goals, the role of science and stakeholders, the initiation and methods of the processes framed by very different cultural, socio-economic and biophysical conditions. Across all three basins, the GMB processes produced a shared understanding among all participants of the major water management issues in the respective river basin and common approaches to address them. The "ownership of the ideas" by the stakeholders, i.e. the topic to be addressed in a GMB process, is important for their willingness to contribute to such a participatory process. Differences, however, exist in so far that cultural and contextual constraints of the basin drive the way the GMB processes have been designed and how their results contribute to policy development.
Koschmann, Jeannette; Bhar, Anirban; Stegmaier, Philip; Kel, Alexander E.; Wingender, Edgar
2015-01-01
A strategy is presented that allows a causal analysis of co-expressed genes, which may be subject to common regulatory influences. A state-of-the-art promoter analysis for potential transcription factor (TF) binding sites in combination with a knowledge-based analysis of the upstream pathway that control the activity of these TFs is shown to lead to hypothetical master regulators. This strategy was implemented as a workflow in a comprehensive bioinformatic software platform. We applied this workflow to gene sets that were identified by a novel triclustering algorithm in naphthalene-induced gene expression signatures of murine liver and lung tissue. As a result, tissue-specific master regulators were identified that are known to be linked with tumorigenic and apoptotic processes. To our knowledge, this is the first time that genes of expression triclusters were used to identify upstream regulators.
Luo, Fei; Timler, Geralyn R
2008-01-01
Studies suggest that the oral narratives of children with attention deficit hyperactivity disorder (ADHD) are less organized than those of typically developing peers. Many studies, however, do not account for children's language abilities. Because language impairment (LI) is a frequent comorbid condition in children with ADHD, this exploratory study investigated language abilities and narrative organization skills in children with and without ADHD. Narratives were elicited using the picture-sequence task and the single-picture task from the Test of Narrative Language (Gillam & Pearson, 2004). The causal network model (Trabasso, Van den Broek, & Suh, 1989) was applied to analyse the narratives. Specifically, narratives were examined to identify complete and incomplete superordinate and subordinate Goal-Attempt-Outcome (GAO) units. The results revealed no differences among the groups in the picture-sequence task. Children with ADHD+LI produced significantly fewer complete superordinate GAO units than typical children in the single-picture task. Theoretical and clinical implications are discussed. PMID:18092218
The Visual Causality Analyst: An Interactive Interface for Causal Reasoning.
Wang, Jun; Mueller, Klaus
2016-01-01
Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst-a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets. PMID:26529703
Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt
2016-01-01
Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling. PMID:26470866
The Technical Hypothesis of Motivational Interviewing: A Meta-Analysis of MI’s Key Causal Model
Magill, Molly; Gaume, Jacques; Apodaca, Timothy R.; Walthers, Justin; Mastroleo, Nadine R.; Borsari, Brian; Longabaugh, Richard
2014-01-01
Objective The technical hypothesis of motivational interviewing (MI) posits that therapist implemented MI skills will be related to client speech regarding behavior change and that client speech will predict client outcome. The current meta-analysis is the first aggregate test of this proposed causal model. Method A systematic literature review, using stringent inclusion criteria, identified k = 16 reports describing 12 primary studies. Review methods calculated the inverse-variance-weighted pooled correlation coefficient for the therapist to client and the client to outcome paths across multiple targeted behaviors (i.e., alcohol or illicit drug use, other addictive behaviors). Results Therapist MI-consistent skills were correlated with more client language in favor of behavior change (i.e., change talk; r = .26, p < .0001), but not less client language against behavior change (i.e., sustain talk; r = .10, p = .09). MI-inconsistent skills were associated with less change talk (r = −.17, p = .001) as well as more sustain talk (r = .07, p = .009). Among these studies, client change talk was not associated with follow-up outcome (r = .06, p = .41), but sustain talk was associated with worse outcome (r = −.24, p = .001). In addition, studies that examined composite client language (e.g., an average of negative and positive statements) showed an overall positive relationship with client behavior change (r = .12, p = .006; k = 6). Conclusions This meta-analysis provides an initial test and partial support for a key causal model of MI efficacy. Recommendations for MI practitioners, clinical supervisors, and process researchers are provided. PMID:24841862
Causal Inference in Public Health
Glass, Thomas A.; Goodman, Steven N.; Hernán, Miguel A.; Samet, Jonathan M.
2014-01-01
Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action’s consequences rather than the less precise notion of a risk factor’s causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world. PMID:23297653
Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte
2015-01-01
A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of “coexistence thinking,” the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one’s range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence), whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking), or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking). Results from three field studies suggest that (a) informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b) they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c) they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross-cultural analyses
Tucker, Bram; Tsiazonera; Tombo, Jaovola; Hajasoa, Patricia; Nagnisaha, Charlotte
2015-01-01
A fact of life for farmers, hunter-gatherers, and fishermen in the rural parts of the world are that crops fail, wild resources become scarce, and winds discourage fishing. In this article we approach subsistence risk from the perspective of "coexistence thinking," the simultaneous application of natural and supernatural causal models to explain subsistence success and failure. In southwestern Madagascar, the ecological world is characterized by extreme variability and unpredictability, and the cosmological world is characterized by anxiety about supernatural dangers. Ecological and cosmological causes seem to point to different risk minimizing strategies: to avoid losses from drought, flood, or heavy winds, one should diversify activities and be flexible; but to avoid losses caused by disrespected spirits one should narrow one's range of behaviors to follow the code of taboos and offerings. We address this paradox by investigating whether southwestern Malagasy understand natural and supernatural causes as occupying separate, contradictory explanatory systems (target dependence), whether they make no categorical distinction between natural and supernatural forces and combine them within a single explanatory system (synthetic thinking), or whether they have separate natural and supernatural categories of causes that are integrated into one explanatory system so that supernatural forces drive natural forces (integrative thinking). Results from three field studies suggest that (a) informants explain why crops, prey, and market activities succeed or fail with reference to natural causal forces like rainfall and pests, (b) they explain why individual persons experience success or failure primarily with supernatural factors like God and ancestors, and (c) they understand supernatural forces as driving natural forces, so that ecology and cosmology represent distinct sets of causes within a single explanatory framework. We expect that future cross-cultural analyses may
Cohomology Methods in Causal Perturbation Theory
NASA Astrophysics Data System (ADS)
Grigore, D. R.
2010-01-01
Various problems in perturbation theory of (quantum) gauge models can be rephrased in the language of cohomology theory. This was already noticed in the functional formulation of perturbative gauge theories. Causal perturbation theory is a fully quantum approach: is works only with the chronological products which are defined as operator-valued distributions in the Fock space of the model. The use of causal perturbation theory leads to similar cohomology problems; the main difference with respect to the functional methods comes from the fact that the gauge transformation of the causal approach is, essentially, the linear part of the non-linear BRST transformation. Using these methods it is possible to give a nice determination of the interaction Lagrangians for gauge models (Yang-Mills and gravitation in the linear approximation); one obtains with this method the unicity of the interaction Lagrangian up to trivial terms. The case of quantum gravity is highly non-trivial and can be generalized with this method to the massive graviton case. Going to higher orders of perturbation theory one finds quantum anomalies. Again the cohomological methods can be used to determine the generic form of these anomalies. Finally, one can investigate the arbitrariness of the chronological products in higher orders and reduce this problem to cohomology methods also.
ERIC Educational Resources Information Center
Platt, Craig W.
1988-01-01
A structural model of the consequences of success attributions--derived from B. Weiner's attribution model--was tested using 208 first-term college students. Although the hypothesized model was rejected based on a chi-square, goodness-of-fit test, a specification search yielded a model that fit the data and was consistent with Weiner's theory.…
Kimura, Daisuke; Nakatani, Ken; Takeda, Tokunori; Fujita, Takashi; Sunahara, Nobuyuki; Inoue, Katsumi; Notoya, Masako
2015-01-01
The purpose of this study is to identify a potentiality factor that is a preventive factor for decline in cognitive function. Additionally, this study pursues to clarify the causal relationship between the each potential factor and its influence on cognitive function. Subjects were 366 elderly community residents (mean age 73.7 ± 6.4, male 51, female 315) who participated in the Taketoyo Project from 2007 to 2011. Factor analysis was conducted to identify groupings within mental, social, life, physical and cognitive functions. In order to detect clusters of 14 variables, the item scores were subjected to confirmatory factor analysis. We performed Structural Equation Modeling analysis to calculate the standardization coefficient and correlation coefficient for every factor. The cause and effect hypothesis model was used to gather two intervention theory hypotheses for dementia prevention (direct effect, indirect effect) in one system. Finally, we performed another Structural Equation Modeling analysis to calculate the standardization of the cause and effect hypothesis model. Social participation was found to be activated by the improvement of four factors, and in turn, activated “Social participation” acted on cognitive function. PMID:25658829
Inferring causal molecular networks: empirical assessment through a community-based effort.
Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach
2016-04-01
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense. PMID:26901648
ERIC Educational Resources Information Center
Bacharach, Samuel; Bamberger, Peter
1992-01-01
Survey data from 215 nurses (10 male) and 430 civil engineers (10 female) supported the plausibility of occupation-specific models (positing direct paths between role stressors, antecedents, and consequences) compared to generic models. A weakness of generic models is the tendency to ignore differences in occupational structure and culture. (SK)
Wu, Guo Rong; Chen, Fuyong; Kang, Dezhi; Zhang, Xiangyang; Marinazzo, Daniele; Chen, Huafu
2011-11-01
Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period. PMID:21788178
ERIC Educational Resources Information Center
Hattori, Masasi; Oaksford, Mike
2007-01-01
In this article, 41 models of covariation detection from 2 x 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in…
A Socio-Psychophysiological Model for Explaining the Causal Effects of Social Reinforcement Systems.
ERIC Educational Resources Information Center
Brown, Edward K.
The expanded socio-psychophysiological model (SPPM) appears to provide a meaningful paradigm for explaining the psycho-psysiological effects of Social Reinforcement Systems (SRS). This model may be used to assist individuals, and the society, to become more aware of the effects that social practices have on the immediate and long-term actions of…
Conen, David; Vollenweider, Peter; Rousson, Valentin; Marques-Vidal, Pedro; Paccaud, Fred; Waeber, Gérard; Bochud, Murielle
2010-12-15
Elevated levels of γ-glutamyltransferase (GGT) have been associated with elevated blood pressure (BP) and diabetes. However, the causality of these relations has not been addressed. The authors performed a cross-sectional analysis (2003-2006) among 4,360 participants from the population-based Cohorte Lausannoise (CoLaus) Study (Lausanne, Switzerland). The rs2017869 variant of the γ-glutamyltransferase 1 (GGT1) gene, which explained 1.6% of the variance in GGT levels, was used as an instrument for Mendelian randomization (MR). Sex-specific GGT quartiles were strongly associated with both systolic and diastolic BP (all P's < 0.0001). After multivariable adjustment, these relations were attenuated but remained significant. Using MR, the authors observed no positive association of GGT with BP (systolic: β -5.68, 95% confidence interval (CI): -11.51, 0.16 (P = 0.06); diastolic: β = -2.24, 95% CI: -5.98, 1.49 (P = 0.24)). The association of GGT with insulin was also attenuated after multivariable adjustment but persisted in the fully adjusted model (β = 0.07, 95% CI: 0.04, 0.09; P < 0.0001). Using MR, the authors also observed a positive association of GGT with insulin (β = 0.19, 95% CI: 0.01, 0.37; P = 0.04). In conclusion, the authors found evidence for a direct causal relation of GGT with fasting insulin but not with BP. PMID:21044991
West, Stephen G.
2016-01-01
Psychologists have long had interest in the processes through which antecedent variables produce their effects on the outcomes of ultimate interest (e.g., Wood-worth's Stimulus-Organism-Response model). Models involving such meditational processes have characterized many of the important psychological theories of the 20th century and continue to the present day. However, it was not until Judd and Kenny (1981) and Baron and Kenny (1986) combined ideas from experimental design and structural equation modeling that statistical methods for directly testing such models, now known as mediation analysis, began to be developed. Methodologists have improved these statistical methods, developing new, more efficient estimators for mediated effects. They have also extended mediation analysis to multilevel data structures, models involving multiple mediators, models in which interactions occur, and an array of noncontinuous outcome measures (see MacKinnon, 2008). This work nicely maps on to key questions of applied researchers and has led to an outpouring of research testing meditational models (As of August, 2011, Baron and Kenny's article has had over 24,000 citations according to Google Scholar). PMID:26736046
Latner, Janet D; Puhl, Rebecca M; Murakami, Jessica M; O'Brien, Kerry S
2014-06-01
The present study examined the impact of the food-addiction model of obesity on weight stigma directed at obese people. Participants (n = 625) were randomly assigned to four experimental conditions. They were asked to read either a food-addiction explanatory model of obesity or a nonaddiction model, and subsequently read a vignette describing a target person who met the characteristics of one of these models and was either obese or of normal weight. Questionnaires assessed participants' stigmatization and blame of targets and their attribution of psychopathology toward targets. Additional questionnaires assessed stigma and blame directed toward obese people generally, and personal fear of fat. A manipulation check revealed that the food-addiction experimental condition did significantly increase belief in the food-addiction model. Significant main effects for addiction showed that the food-addiction model produced less stigma, less blame, and lower perceived psychopathology attributed to the target described in vignettes, regardless of the target's weight. The food-addiction model also produced less blame toward obese people in general and less fear of fat. The present findings suggest that presenting obesity as an addiction does not increase weight bias and could even be helpful in reducing the widespread prejudice against obese people. PMID:24630939
ERIC Educational Resources Information Center
Marsh, Herbert W.; Trautwein, Ulrich; Ldtke, Oliver; Kller, Olaf; Baumert, Jrgen
2005-01-01
Reciprocal effects models of longitudinal data show that academic self-concept is both a cause and an effect of achievement. In this study this model was extended to juxtapose self-concept with academic interest. Based on longitudinal data from 2 nationally representative samples of German 7th-grade students (Study 1: N=5,649, M age13.4; Study 2:…
Gilbert, Jessica R.; Symmonds, Mkael; Hanna, Michael G.; Dolan, Raymond J.; Friston, Karl J.; Moran, Rosalyn J.
2016-01-01
Clinical assessments of brain function rely upon visual inspection of electroencephalographic waveform abnormalities in tandem with functional magnetic resonance imaging. However, no current technology proffers in vivo assessments of activity at synapses, receptors and ion-channels, the basis of neuronal communication. Using dynamic causal modeling we compared electrophysiological responses from two patients with distinct monogenic ion channelopathies and a large cohort of healthy controls to demonstrate the feasibility of assaying synaptic-level channel communication non-invasively. Synaptic channel abnormality was identified in both patients (100% sensitivity) with assay specificity above 89%, furnishing estimates of neurotransmitter and voltage-gated ion throughput of sodium, calcium, chloride and potassium. This performance indicates a potential novel application as an adjunct for clinical assessments in neurological and psychiatric settings. More broadly, these findings indicate that biophysical models of synaptic channels can be estimated non-invasively, having important implications for advancing human neuroimaging to the level of non-invasive ion channel assays. PMID:26342528
Causal tapestries for psychology and physics.
Sulis, William H
2012-04-01
Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries. PMID:22452929
Adams, Rick A; Bauer, Markus; Pinotsis, Dimitris; Friston, Karl J
2016-05-15
This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision - inferred by our behavioural DCM - correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia. PMID:26921713
Adams, Rick A.; Bauer, Markus; Pinotsis, Dimitris; Friston, Karl J.
2016-01-01
This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia. PMID:26921713
ERIC Educational Resources Information Center
Possel, Patrick; Seemann, Simone; Ahrens, Stefanie; Hautzinger, Martin
2006-01-01
In Dodge's model of "social information processing" depression is the result of a linear sequence of five stages of information processing ("Annu Rev Psychol" 44: 559-584, 1993). These stages follow a person's reaction to situational stimuli, such that each stage of information processing mediates the relationship between earlier and later stages.…
Residential Segregation of Blacks and Racial Inequality in Southern Cities: Toward a Causal Model.
ERIC Educational Resources Information Center
Roof, W. Clark
This study explores how residential segregation can be thought of in terms of an economic competition theory of minority-group relations. The model proposed is considered applicable to the American South, and with some modification, relevant to other settings. The objectives are: (1) to show that residential segregation indices are related to…
ERIC Educational Resources Information Center
Rindermann, H.; Neubauer, A. C.
2004-01-01
According to mental speed theory of intelligence, the speed of information processing constitutes an important basis for cognitive abilities. However, the question, how mental speed relates to real world criteria, like school, academic, or job performance, is still unanswered. The aim of the study is to test an indirect speed-factor model in…
Causal Client Models in Selecting Effective Interventions: A Cognitive Mapping Study
ERIC Educational Resources Information Center
de Kwaadsteniet, Leontien; Hagmayer, York; Krol, Nicole P. C. M.; Witteman, Cilia L. M.
2010-01-01
An important reason to choose an intervention to treat psychological problems of clients is the expectation that the intervention will be effective in alleviating the problems. The authors investigated whether clinicians base their ratings of the effectiveness of interventions on models that they construct representing the factors causing and…
[Causality in occupational health: the Ardystil case].
García García, A M; Benavides, F G
1995-01-01
Establishing causal relationships has been and is today a matter of debate in epidemiology. The observational nature of epidemiological research rends difficult the proving of these relationships. Related to this, different models and causal criteria have been proposed in order to explain health and disease determinants, from pure determinism in Koch postulates, accepting unicausal explanation for diseases, to more realistic multicausal models. In occupational health it is necessary to formulate causal models and criteria to assess causality, and frequently causal assessment in this field has important social, economic and juridical relevance. This paper deal with evaluation of causal relationships in epidemiology and this evaluation is illustrated with a recent example of an occupational health problem in our milieu: the Ardystil case. PMID:8666516
Linking service climate and customer perceptions of service quality: test of a causal model.
Schneider, B; White, S S; Paul, M C
1998-04-01
A set of foundation issues that support employee work and service quality is conceptualized as a necessary but not sufficient cause of a climate for service, which in turn is proposed to be reflected in customer experiences. Climate for service rests on the foundation issues, but in addition it requires policies and practices that focus attention directly on service quality. Data were collected at multiple points in time from employees and customers of 134 branches of a bank and analyzed via structural equation modeling. Results indicated that the model in which the foundation issues yielded a climate for service, and climate for service in turn led to customer perceptions of service quality, fit the data well. However, subsequent cross-lagged analyses revealed the presence of a reciprocal effect for climate and customer perceptions. Implications of these results for theory and research are offered. PMID:9577232
Inversion-based control of a vehicle with a clutch using a switched causal modelling
NASA Astrophysics Data System (ADS)
Lhomme, W.; Trigui, R.; Bouscayrol, A.; Delarue, P.; Jeanneret, B.; Badin, F.
2011-02-01
The modelling of a clutch in a power train transmission is a delicate process because of its non-linear behaviour. Two different states have to be taken into account: when the clutch is locked and when the clutch is slipping. Moreover the clutch has often to be controlled automatically in parallel hybrid electric vehicles (HEVs). An energetic macroscopic representation (EMR) of a clutch system has been developed. Both clutch states are genuinely taken into account in a physical way. In this article, EMR leads to organise the control scheme of the clutch system using an inversion methodology. An experimental validation is provided on a conventional vehicle before being implemented on parallel HEVs. Experimental results are provided to validate the clutch model and the inversion-based control.
NASA Astrophysics Data System (ADS)
Wissner-Gross, A. D.; Freer, C. E.
2013-04-01
Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.
Xi, Yi-Bin; Li, Chen; Cui, Long-Biao; Liu, Jian; Guo, Fan; Li, Liang; Liu, Ting-Ting; Liu, Kang; Chen, Gang; Xi, Min; Wang, Hua-Ning; Yin, Hong
2016-01-01
Familial risk plays a significant role in the etiology of schizophrenia (SZ). Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC), caudate, dorsolateral prefrontal cortex (DLPFC), and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs) in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients-according to the DSM-IV-were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI). We used stochastic dynamic causal modeling (sDCM) to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA) to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ. PMID:27512370
Xi, Yi-Bin; Li, Chen; Cui, Long-Biao; Liu, Jian; Guo, Fan; Li, Liang; Liu, Ting-Ting; Liu, Kang; Chen, Gang; Xi, Min; Wang, Hua-Ning; Yin, Hong
2016-01-01
Familial risk plays a significant role in the etiology of schizophrenia (SZ). Many studies using neuroimaging have demonstrated structural and functional alterations in relatives of SZ patients, with significant results found in diverse brain regions involving the anterior cingulate cortex (ACC), caudate, dorsolateral prefrontal cortex (DLPFC), and hippocampus. This study investigated whether unaffected relatives of first episode SZ differ from healthy controls (HCs) in effective connectivity measures among these regions. Forty-six unaffected first-degree relatives of first episode SZ patients—according to the DSM-IV—were studied. Fifty HCs were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI). We used stochastic dynamic causal modeling (sDCM) to estimate the directed connections between the left ACC, right ACC, left caudate, right caudate, left DLPFC, left hippocampus, and right hippocampus. We used Bayesian parameter averaging (BPA) to characterize the differences. The BPA results showed hyperconnectivity from the left ACC to right hippocampus and hypoconnectivity from the right ACC to right hippocampus in SZ relatives compared to HCs. The pattern of anterior cingulate cortico-hippocampal connectivity in SZ relatives may be a familial feature of SZ risk, appearing to reflect familial susceptibility for SZ. PMID:27512370
Causal Inference and Observational Research: The Utility of Twins
McGue, Matt; Osler, Merete; Christensen, Kaare
2011-01-01
Valid causal inference is central to progress in theoretical and applied psychology. Although the randomized experiment is widely considered the gold standard for determining whether a given exposure increases the likelihood of some specified outcome, experiments are not always feasible and in some cases can result in biased estimates of causal effects. Alternatively, standard observational approaches are limited by the possibility of confounding, reverse causation, and the nonrandom distribution of exposure (i.e., selection). We describe the counterfactual model of causation and apply it to the challenges of causal inference in observational research, with a particular focus on aging. We argue that the study of twin pairs discordant on exposure, and in particular discordant monozygotic twins, provides a useful analog to the idealized counterfactual design. A review of discordant-twin studies in aging reveals that they are consistent with, but do not unambiguously establish, a causal effect of lifestyle factors on important late-life outcomes. Nonetheless, the existing studies are few in number and have clear limitations that have not always been considered in interpreting their results. It is concluded that twin researchers could make greater use of the discordant-twin design as one approach to strengthen causal inferences in observational research. PMID:21593989
A model comparison approach shows stronger support for economic models of fertility decline
Shenk, Mary K.; Towner, Mary C.; Kress, Howard C.; Alam, Nurul
2013-01-01
The demographic transition is an ongoing global phenomenon in which high fertility and mortality rates are replaced by low fertility and mortality. Despite intense interest in the causes of the transition, especially with respect to decreasing fertility rates, the underlying mechanisms motivating it are still subject to much debate. The literature is crowded with competing theories, including causal models that emphasize (i) mortality and extrinsic risk, (ii) the economic costs and benefits of investing in self and children, and (iii) the cultural transmission of low-fertility social norms. Distinguishing between models, however, requires more comprehensive, better-controlled studies than have been published to date. We use detailed demographic data from recent fieldwork to determine which models produce the most robust explanation of the rapid, recent demographic transition in rural Bangladesh. To rigorously compare models, we use an evidence-based statistical approach using model selection techniques derived from likelihood theory. This approach allows us to quantify the relative evidence the data give to alternative models, even when model predictions are not mutually exclusive. Results indicate that fertility, measured as either total fertility or surviving children, is best explained by models emphasizing economic factors and related motivations for parental investment. Our results also suggest important synergies between models, implicating multiple causal pathways in the rapidity and degree of recent demographic transitions. PMID:23630293
A model comparison approach shows stronger support for economic models of fertility decline.
Shenk, Mary K; Towner, Mary C; Kress, Howard C; Alam, Nurul
2013-05-14
The demographic transition is an ongoing global phenomenon in which high fertility and mortality rates are replaced by low fertility and mortality. Despite intense interest in the causes of the transition, especially with respect to decreasing fertility rates, the underlying mechanisms motivating it are still subject to much debate. The literature is crowded with competing theories, including causal models that emphasize (i) mortality and extrinsic risk, (ii) the economic costs and benefits of investing in self and children, and (iii) the cultural transmission of low-fertility social norms. Distinguishing between models, however, requires more comprehensive, better-controlled studies than have been published to date. We use detailed demographic data from recent fieldwork to determine which models produce the most robust explanation of the rapid, recent demographic transition in rural Bangladesh. To rigorously compare models, we use an evidence-based statistical approach using model selection techniques derived from likelihood theory. This approach allows us to quantify the relative evidence the data give to alternative models, even when model predictions are not mutually exclusive. Results indicate that fertility, measured as either total fertility or surviving children, is best explained by models emphasizing economic factors and related motivations for parental investment. Our results also suggest important synergies between models, implicating multiple causal pathways in the rapidity and degree of recent demographic transitions. PMID:23630293
ERIC Educational Resources Information Center
Jeong, Allan; Lee, Woon Jee
2012-01-01
This study examined some of the methodological approaches used by students to construct causal maps in order to determine which approaches help students understand the underlying causes and causal mechanisms in a complex system. This study tested the relationship between causal understanding (ratio of root causes correctly/incorrectly identified,…
Hillebrandt, Hauke; Friston, Karl J.; Blakemore, Sarah-Jayne
2014-01-01
Biological agents are the most complex systems humans have to model and predict. In predictive coding, high-level cortical areas inform sensory cortex about incoming sensory signals, a comparison between the predicted and actual sensory feedback is made, and information about unpredicted sensory information is passed forward to higher-level areas. Predictions about animate motion – relative to inanimate motion – should result in prediction error and increase signal passing from lower level sensory area MT+/V5, which is responsive to all motion, to higher-order posterior superior temporal sulcus (pSTS), which is selectively activated by animate motion. We tested this hypothesis by investigating effective connectivity in a large-scale fMRI dataset from the Human Connectome Project. 132 participants viewed animations of triangles that were designed to move in a way that appeared animate (moving intentionally), or inanimate (moving in a mechanical way). We found that forward connectivity from V5 to the pSTS increased, and inhibitory self-connection in the pSTS decreased, when viewing intentional motion versus inanimate motion. These prediction errors associated with animate motion may be the cause for increased attention to animate stimuli found in previous studies. PMID:25174814
Hillebrandt, Hauke; Friston, Karl J; Blakemore, Sarah-Jayne
2014-01-01
Biological agents are the most complex systems humans have to model and predict. In predictive coding, high-level cortical areas inform sensory cortex about incoming sensory signals, a comparison between the predicted and actual sensory feedback is made, and information about unpredicted sensory information is passed forward to higher-level areas. Predictions about animate motion - relative to inanimate motion - should result in prediction error and increase signal passing from lower level sensory area MT+/V5, which is responsive to all motion, to higher-order posterior superior temporal sulcus (pSTS), which is selectively activated by animate motion. We tested this hypothesis by investigating effective connectivity in a large-scale fMRI dataset from the Human Connectome Project. 132 participants viewed animations of triangles that were designed to move in a way that appeared animate (moving intentionally), or inanimate (moving in a mechanical way). We found that forward connectivity from V5 to the pSTS increased, and inhibitory self-connection in the pSTS decreased, when viewing intentional motion versus inanimate motion. These prediction errors associated with animate motion may be the cause for increased attention to animate stimuli found in previous studies. PMID:25174814
A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression
Nicolaou, Nicoletta; Constandinou, Timothy G.
2016-01-01
Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, CNPMR, Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply CNPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). CNPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. CNPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. PMID:27378901
Nonparametric causal inference for bivariate time series
NASA Astrophysics Data System (ADS)
McCracken, James M.; Weigel, Robert S.
2016-02-01
We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.
Statistical Analysis of Single-Trial Granger Causality Spectra
Brovelli, Andrea
2012-01-01
Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity. PMID:22649482
Fluctuations in relativistic causal hydrodynamics
NASA Astrophysics Data System (ADS)
Kumar, Avdhesh; Bhatt, Jitesh R.; Mishra, Ananta P.
2014-05-01
Formalism to calculate the hydrodynamic fluctuations by applying the Onsager theory to the relativistic Navier-Stokes equation is already known. In this work, we calculate hydrodynamic fluctuations within the framework of the second order hydrodynamics of Müller, Israel and Stewart and its generalization to the third order. We have also calculated the fluctuations for several other causal hydrodynamical equations. We show that the form for the Onsager-coefficients and form of the correlation functions remain the same as those obtained by the relativistic Navier-Stokes equation and do not depend on any specific model of hydrodynamics. Further we numerically investigate evolution of the correlation function using the one dimensional boost-invariant (Bjorken) flow. We compare the correlation functions obtained using the causal hydrodynamics with the correlation function for the relativistic Navier-Stokes equation. We find that the qualitative behavior of the correlation functions remains the same for all the models of the causal hydrodynamics.
Causality and cosmic inflation
Vachaspati, Tanmay; Trodden, Mark
2000-01-15
In the context of inflationary models with a pre-inflationary stage, in which the Einstein equations are obeyed, the null energy condition is satisfied, and spacetime topology is trivial, we argue that homogeneity on super-Hubble scales must be assumed as an initial condition. Models in which inflation arises from field dynamics in a Friedmann-Robertson-Walker background fall into this class but models in which inflation originates at the Planck epoch may evade this conclusion. Our arguments rest on causality and general relativistic constraints on the structure of spacetime. We discuss modifications to existing scenarios that may avoid the need for initial large-scale homogeneity. (c) 1999 The American Physical Society.
Bayesian approach for network modeling of brain structural features
NASA Astrophysics Data System (ADS)
Joshi, Anand A.; Joshi, Shantanu H.; Leahy, Richard M.; Shattuck, David W.; Dinov, Ivo; Toga, Arthur W.
2010-03-01
Brain connectivity patterns are useful in understanding brain function and organization. Anatomical brain connectivity is largely determined using the physical synaptic connections between neurons. In contrast statistical brain connectivity in a given brain population refers to the interaction and interdependencies of statistics of multitudes of brain features including cortical area, volume, thickness etc. Traditionally, this dependence has been studied by statistical correlations of cortical features. In this paper, we propose the use of Bayesian network modeling for inferring statistical brain connectivity patterns that relate to causal (directed) as well as non-causal (undirected) relationships between cortical surface areas. We argue that for multivariate cortical data, the Bayesian model provides for a more accurate representation by removing the effect of confounding correlations that get introduced due to canonical dependence between the data. Results are presented for a population of 466 brains, where a SEM (structural equation modeling) approach is used to generate a Bayesian network model, as well as a dependency graph for the joint distribution of cortical areas.
Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng
2015-01-01
The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches. PMID:26434769
Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng
2015-01-01
The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches. PMID:26434769
NASA Astrophysics Data System (ADS)
Li, Fali; Tian, Yin; Zhang, Yangsong; Qiu, Kan; Tian, Chunyang; Jing, Wei; Liu, Tiejun; Xia, Yang; Guo, Daqing; Yao, Dezhong; Xu, Peng
2015-10-01
The neural mechanism of steady-state visual evoked potentials (SSVEP) is still not clearly understood. Especially, only certain frequency stimuli can evoke SSVEP. Our previous network study reveals that 8 Hz stimulus that can evoke strong SSVEP response shows the enhanced linkage strength between frontal and visual cortex. To further probe the directed information flow between the two cortex areas for various frequency stimuli, this paper develops a causality analysis based on the inversion of double columns model using particle swarm optimization (PSO) to characterize the directed information flow between visual and frontal cortices with the intracranial rat electroencephalograph (EEG). The estimated model parameters demonstrate that the 8 Hz stimulus shows the enhanced directional information flow from visual cortex to frontal lobe facilitates SSVEP response, which may account for the strong SSVEP response for 8 Hz stimulus. Furthermore, the similar finding is replicated by data-driven causality analysis. The inversion of neural mass model proposed in this study may be helpful to provide the new causality analysis to link the physiological model and the observed datasets in neuroscience and clinical researches.
Modeling approaches for active systems
NASA Astrophysics Data System (ADS)
Herold, Sven; Atzrodt, Heiko; Mayer, Dirk; Thomaier, Martin
2006-03-01
To solve a wide range of vibration problems with the active structures technology, different simulation approaches for several models are needed. The selection of an appropriate modeling strategy is depending, amongst others, on the frequency range, the modal density and the control target. An active system consists of several components: the mechanical structure, at least one sensor and actuator, signal conditioning electronics and the controller. For each individual part of the active system the simulation approaches can be different. To integrate the several modeling approaches into an active system simulation and to ensure a highly efficient and accurate calculation, all sub models must harmonize. For this purpose, structural models considered in this article are modal state-space formulations for the lower frequency range and transfer function based models for the higher frequency range. The modal state-space formulations are derived from finite element models and/or experimental modal analyses. Consequently, the structure models which are based on transfer functions are directly derived from measurements. The transfer functions are identified with the Steiglitz-McBride iteration method. To convert them from the z-domain to the s-domain a least squares solution is implemented. An analytical approach is used to derive models of active interfaces. These models are transferred into impedance formulations. To couple mechanical and electrical sub-systems with the active materials, the concept of impedance modeling was successfully tested. The impedance models are enhanced by adapting them to adequate measurements. The controller design strongly depends on the frequency range and the number of modes to be controlled. To control systems with a small number of modes, techniques such as active damping or independent modal space control may be used, whereas in the case of systems with a large number of modes or with modes that are not well separated, other control
Interpretational Confounding or Confounded Interpretations of Causal Indicators?
Bainter, Sierra A.; Bollen, Kenneth A.
2014-01-01
In measurement theory causal indicators are controversial and little-understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning intended by a researcher. This article questions the validity of evidence used to claim that causal indicators are inherently susceptible to interpretational confounding. Further, a simulation study demonstrates that causal indicator coefficients are stable across correctly-specified models. Determining the suitability of causal indicators has implications for the way we conceptualize measurement and build and evaluate measurement models. PMID:25530730
Saladié, Òscar; Santos-Lacueva, Raquel
2016-02-01
One of the main objectives of municipal waste management policies is to improve separate collection, both quantitatively and qualitatively. Several factors influence people behavior to recycling and, consequently, they play an important role to achieve the goals proposed in the management policies. People can improve separate collection rates because of a wide range of causes with different weight. Here, we have determined the uplift in probability to improve separate collection of municipal waste created by the awareness campaigns among 806 undergraduate students at Universitat Rovira i Virgili (Catalonia) by means of the Causal Chain Approach, a probabilistic method. A 73.2% state having improved separate collection in recent years and the most of them (75.4%) remember some awareness campaign. The results show the uplift in probability to improve separate collection attributable to the awareness campaigns is 17.9%. They should be taken into account by policy makers in charge of municipal waste management. Nevertheless, it must be assumed an awareness campaign will never be sufficient to achieve the objectives defined in municipal waste management programmes. PMID:26684055
Sensitivity analyses for parametric causal mediation effect estimation.
Albert, Jeffrey M; Wang, Wei
2015-04-01
Causal mediation analysis uses a potential outcomes framework to estimate the direct effect of an exposure on an outcome and its indirect effect through an intermediate variable (or mediator). Causal interpretations of these effects typically rely on sequential ignorability. Because this assumption is not empirically testable, it is important to conduct sensitivity analyses. Sensitivity analyses so far offered for this situation have either focused on the case where the outcome follows a linear model or involve nonparametric or semiparametric models. We propose alternative approaches that are suitable for responses following generalized linear models. The first approach uses a Gaussian copula model involving latent versions of the mediator and the final outcome. The second approach uses a so-called hybrid causal-observational model that extends the association model for the final outcome, providing a novel sensitivity parameter. These models, while still assuming a randomized exposure, allow for unobserved (as well as observed) mediator-outcome confounders that are not affected by exposure. The methods are applied to data from a study of the effect of mother education on dental caries in adolescence. PMID:25395683
Sensitivity analyses for parametric causal mediation effect estimation
Albert, Jeffrey M.; Wang, Wei
2015-01-01
Causal mediation analysis uses a potential outcomes framework to estimate the direct effect of an exposure on an outcome and its indirect effect through an intermediate variable (or mediator). Causal interpretations of these effects typically rely on sequential ignorability. Because this assumption is not empirically testable, it is important to conduct sensitivity analyses. Sensitivity analyses so far offered for this situation have either focused on the case where the outcome follows a linear model or involve nonparametric or semiparametric models. We propose alternative approaches that are suitable for responses following generalized linear models. The first approach uses a Gaussian copula model involving latent versions of the mediator and the final outcome. The second approach uses a so-called hybrid causal-observational model that extends the association model for the final outcome, providing a novel sensitivity parameter. These models, while still assuming a randomized exposure, allow for unobserved (as well as observed) mediator-outcome confounders that are not affected by exposure. The methods are applied to data from a study of the effect of mother education on dental caries in adolescence. PMID:25395683
Multisource causal data mining
NASA Astrophysics Data System (ADS)
Woodley, Robert; Gosnell, Michael; Shallenberger, Kevin
2012-06-01
Analysts are faced with mountains of data, and finding that relevant piece of information is the proverbial needle in a haystack, only with dozens of haystacks. Analysis tools that facilitate identifying causal relationships across multiple data sets are sorely needed. 21st Century Systems, Inc. (21CSi) has initiated research called Causal-View, a causal datamining visualization tool, to address this challenge. Causal-View is built on an agent-enabled framework. Much of the processing that Causal-View will do is in the background. When a user requests information, Data Extraction Agents launch to gather information. This initial search is a raw, Monte Carlo type search designed to gather everything available that may have relevance to an individual, location, associations, and more. This data is then processed by Data- Mining Agents. The Data-Mining Agents are driven by user supplied feature parameters. If the analyst is looking to see if the individual frequents a known haven for insurgents he may request information on his last known locations. Or, if the analyst is trying to see if there is a pattern in the individual's contacts, the mining agent can be instructed with the type and relevance of the information fields to look at. The same data is extracted from the database, but the Data Mining Agents customize the feature set to determine causal relationships the user is interested in. At this point, a Hypothesis Generation and Data Reasoning Agents take over to form conditional hypotheses about the data and pare the data, respectively. The newly formed information is then published to the agent communication backbone of Causal- View to be displayed. Causal-View provides causal analysis tools to fill the gaps in the causal chain. We present here the Causal-View concept, the initial research into data mining tools that assist in forming the causal relationships, and our initial findings.
NASA Technical Reports Server (NTRS)
Moray, Neville; King, Barbara; Turksen, Burhan; Waterton, Keith
1987-01-01
Fuzzy and crisp measurements of workload are compared for a tracking task that varied in bandwidth and order of control. Fuzzy measures are as powerful as crisp measures, and can under certain conditions give extra insights into workload causality. Both methods suggest that workload arises in a system in which effort, performance, difficulty, and task variables are linked in a closed loop. Marked individual differences were found. Future work on the fuzzy measurement of workload is justified.
Recursive partitioning for heterogeneous causal effects.
Athey, Susan; Imbens, Guido
2016-07-01
In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without "sparsity" assumptions. We propose an "honest" approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the "ground truth" for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7-22%. PMID:27382149
Recursive partitioning for heterogeneous causal effects
Athey, Susan; Imbens, Guido
2016-01-01
In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%. PMID:27382149
Causal Learning Across Domains
ERIC Educational Resources Information Center
Schulz, Laura E.; Gopnik, Alison
2004-01-01
Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of…
Causality in Classical Electrodynamics
ERIC Educational Resources Information Center
Savage, Craig
2012-01-01
Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…
Repeated Causal Decision Making
ERIC Educational Resources Information Center
Hagmayer, York; Meder, Bjorn
2013-01-01
Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in…
Witnessing causal nonseparability
NASA Astrophysics Data System (ADS)
Araújo, Mateus; Branciard, Cyril; Costa, Fabio; Feix, Adrien; Giarmatzi, Christina; Brukner, Časlav
2015-10-01
Our common understanding of the physical world deeply relies on the notion that events are ordered with respect to some time parameter, with past events serving as causes for future ones. Nonetheless, it was recently found that it is possible to formulate quantum mechanics without any reference to a global time or causal structure. The resulting framework includes new kinds of quantum resources that allow performing tasks—in particular, the violation of causal inequalities—which are impossible for events ordered according to a global causal order. However, no physical implementation of such resources is known. Here we show that a recently demonstrated resource for quantum computation—the quantum switch—is a genuine example of ‘indefinite causal order’. We do this by introducing a new tool—the causal witness—which can detect the causal nonseparability of any quantum resource that is incompatible with a definite causal order. We show however that the quantum switch does not violate any causal inequality.
Widlok, Thomas
2014-01-01
Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition. PMID:25414683
Guo, Hui; Fortune, Mary D; Burren, Oliver S; Schofield, Ellen; Todd, John A; Wallace, Chris
2015-06-15
The genes and cells that mediate genetic associations identified through genome-wide association studies (GWAS) are only partially understood. Several studies that have investigated the genetic regulation of gene expression have shown that disease-associated variants are over-represented amongst expression quantitative trait loci (eQTL) variants. Evidence for colocalisation of eQTL and disease causal variants can suggest causal genes and cells for these genetic associations. Here, we used colocalisation analysis to investigate whether 595 genetic associations to ten immune-mediated diseases are consistent with a causal variant that regulates, in cis, gene expression in resting B cells, and in resting and stimulated monocytes. Previously published candidate causal genes were over-represented amongst genes exhibiting colocalisation (odds ratio > 1.5), and we identified evidence for colocalisation (posterior odds > 5) between cis eQTLs in at least one cell type and at least one disease for six genes: ADAM15, RGS1, CARD9, LTBR, CTSH and SYNGR1. We identified cell-specific effects, such as for CTSH, the expression of which in monocytes, but not in B cells, may mediate type 1 diabetes and narcolepsy associations in the chromosome 15q25.1 region. Our results demonstrate the utility of integrating genetic studies of disease and gene expression for highlighting causal genes and cell types. PMID:25743184
Guo, Hui; Fortune, Mary D.; Burren, Oliver S.; Schofield, Ellen; Todd, John A.; Wallace, Chris
2015-01-01
The genes and cells that mediate genetic associations identified through genome-wide association studies (GWAS) are only partially understood. Several studies that have investigated the genetic regulation of gene expression have shown that disease-associated variants are over-represented amongst expression quantitative trait loci (eQTL) variants. Evidence for colocalisation of eQTL and disease causal variants can suggest causal genes and cells for these genetic associations. Here, we used colocalisation analysis to investigate whether 595 genetic associations to ten immune-mediated diseases are consistent with a causal variant that regulates, in cis, gene expression in resting B cells, and in resting and stimulated monocytes. Previously published candidate causal genes were over-represented amongst genes exhibiting colocalisation (odds ratio > 1.5), and we identified evidence for colocalisation (posterior odds > 5) between cis eQTLs in at least one cell type and at least one disease for six genes: ADAM15, RGS1, CARD9, LTBR, CTSH and SYNGR1. We identified cell-specific effects, such as for CTSH, the expression of which in monocytes, but not in B cells, may mediate type 1 diabetes and narcolepsy associations in the chromosome 15q25.1 region. Our results demonstrate the utility of integrating genetic studies of disease and gene expression for highlighting causal genes and cell types. PMID:25743184
Causality in physiological signals.
Müller, Andreas; Kraemer, Jan F; Penzel, Thomas; Bonnemeier, Hendrik; Kurths, Jürgen; Wessel, Niels
2016-05-01
Health is one of the most important non-material assets and thus also has an enormous influence on material values, since treating and preventing diseases is expensive. The number one cause of death worldwide today originates in cardiovascular diseases. For these reasons the aim of understanding the functions and the interactions of the cardiovascular system is and has been a major research topic throughout various disciplines for more than a hundred years. The purpose of most of today's research is to get as much information as possible with the lowest possible effort and the least discomfort for the subject or patient, e.g. via non-invasive measurements. A family of tools whose importance has been growing during the last years is known under the headline of coupling measures. The rationale for this kind of analysis is to identify the structure of interactions in a system of multiple components. Important information lies for example in the coupling direction, the coupling strength, and occurring time lags. In this work, we will, after a brief general introduction covering the development of cardiovascular time series analysis, introduce, explain and review some of the most important coupling measures and classify them according to their origin and capabilities in the light of physiological analyses. We will begin with classical correlation measures, go via Granger-causality-based tools, entropy-based techniques (e.g. momentary information transfer), nonlinear prediction measures (e.g. mutual prediction) to symbolic dynamics (e.g. symbolic coupling traces). All these methods have contributed important insights into physiological interactions like cardiorespiratory coupling, neuro-cardio-coupling and many more. Furthermore, we will cover tools to detect and analyze synchronization and coordination (e.g. synchrogram and coordigram). As a last point we will address time dependent couplings as identified using a recent approach employing ensembles of time series. The
HEDR modeling approach: Revision 1
Shipler, D.B.; Napier, B.A.
1994-05-01
This report is a revision of the previous Hanford Environmental Dose Reconstruction (HEDR) Project modeling approach report. This revised report describes the methods used in performing scoping studies and estimating final radiation doses to real and representative individuals who lived in the vicinity of the Hanford Site. The scoping studies and dose estimates pertain to various environmental pathways during various periods of time. The original report discussed the concepts under consideration in 1991. The methods for estimating dose have been refined as understanding of existing data, the scope of pathways, and the magnitudes of dose estimates were evaluated through scoping studies.
Granger causality and Atlantic hurricanes
NASA Astrophysics Data System (ADS)
Elsner, James B.
2007-08-01
Atlantic tropical cyclones have been getting stronger recently with a trend that is related to an increase in the late summer/early fall sea-surface temperature over the North Atlantic. Some studies attribute the increasing ocean warmth and hurricane intensity to a natural climate fluctuation, known as the Atlantic Multidecadal Oscillation; others suggest that climate change related to anthropogenic greenhouse gases emissions is the cause. Noting that the only difference between these two hypotheses is the causal connection between global mean near-surface air temperature (GT) and Atlantic sea-surface temperature (SST), the author previously showed how to use statistical tests to examine this hypothesis. Here the author expands on this research. In particular, a more comprehensive explanation of the techniques and additional tests and checks against misspecification are provided. The earlier results are confirmed in showing that preceding GT anomalies have a significant statistical relationship to current SST anomalies but not conversely so that if causality exists between Atlantic SST and global temperature, the causal direction likely goes from GT to SST. The result is robust against a small amount of noise added to the data. Identical tests applied to surrogate time series fail to identify causality as expected. The work underscores the importance of using data models to understand relationships between hurricanes and climate.
ERIC Educational Resources Information Center
Johnson, Samuel G. B.; Ahn, Woo-kyoung
2015-01-01
Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…
Generalized Causal Mediation Analysis
Albert, Jeffrey M.; Nelson, Suchitra
2010-01-01
Summary The goal of mediation analysis is to assess direct and indirect effects of a treatment or exposure on an outcome. More generally, we may be interested in the context of a causal model as characterized by a directed acyclic graph (DAG), where mediation via a specific path from exposure to outcome may involve an arbitrary number of links (or ‘stages’). Methods for estimating mediation (or pathway) effects are available for a continuous outcome and a continuous mediator related via a linear model, while for a categorical outcome or categorical mediator, methods are usually limited to two-stage mediation. We present a method applicable to multiple stages of mediation and mixed variable types using generalized linear models. We define pathway effects using a potential outcomes framework and present a general formula that provides the effect of exposure through any specified pathway. Some pathway effects are nonidentifiable and their estimation requires an assumption regarding the correlation between counterfactuals. We provide a sensitivity analysis to assess of the impact of this assumption. Confidence intervals for pathway effect estimates are obtained via a bootstrap method. The method is applied to a cohort study of dental caries in very low birth weight adolescents. A simulation study demonstrates low bias of pathway effect estimators and close-to-nominal coverage rates of confidence intervals. We also find low sensitivity to the counterfactual correlation in most scenarios. PMID:21306353
How to Be Causal: Time, Spacetime and Spectra
ERIC Educational Resources Information Center
Kinsler, Paul
2011-01-01
I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…
Quantifying information transfer and mediation along causal pathways in complex systems
NASA Astrophysics Data System (ADS)
Runge, Jakob
2015-12-01
Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer aimed at decompositions of predictive information about a target variable, while excluding effects of common drivers and indirect influences. While common drivers clearly constitute a spurious causality, the aim of the present article is to develop measures quantifying different notions of the strength of information transfer along indirect causal paths, based on first reconstructing the multivariate causal network. Another class of novel measures quantifies to what extent different intermediate processes on causal paths contribute to an interaction mechanism to determine pathways of causal information transfer. The proposed framework complements predictive decomposition schemes by focusing more on the interaction mechanism between multiple processes. A rigorous mathematical framework allows for a clear information-theoretic interpretation that can also be related to the underlying dynamics as proven for certain classes of processes. Generally, however, estimates of information transfer remain hard to interpret for nonlinearly intertwined complex systems. But if experiments or mathematical models are not available, then measuring pathways of information transfer within the causal dependency structure allows at least for an abstraction of the dynamics. The measures are illustrated on a climatological example to disentangle pathways of atmospheric flow over Europe.
Quantifying information transfer and mediation along causal pathways in complex systems.
Runge, Jakob
2015-12-01
Measures of information transfer have become a popular approach to analyze interactions in complex systems such as the Earth or the human brain from measured time series. Recent work has focused on causal definitions of information transfer aimed at decompositions of predictive information about a target variable, while excluding effects of common drivers and indirect influences. While common drivers clearly constitute a spurious causality, the aim of the present article is to develop measures quantifying different notions of the strength of information transfer along indirect causal paths, based on first reconstructing the multivariate causal network. Another class of novel measures quantifies to what extent different intermediate processes on causal paths contribute to an interaction mechanism to determine pathways of causal information transfer. The proposed framework complements predictive decomposition schemes by focusing more on the interaction mechanism between multiple processes. A rigorous mathematical framework allows for a clear information-theoretic interpretation that can also be related to the underlying dynamics as proven for certain classes of processes. Generally, however, estimates of information transfer remain hard to interpret for nonlinearly intertwined complex systems. But if experiments or mathematical models are not available, then measuring pathways of information transfer within the causal dependency structure allows at least for an abstraction of the dynamics. The measures are illustrated on a climatological example to disentangle pathways of atmospheric flow over Europe. PMID:26764766
Modeling Approaches in Planetary Seismology
NASA Technical Reports Server (NTRS)
Weber, Renee; Knapmeyer, Martin; Panning, Mark; Schmerr, Nick
2014-01-01
Of the many geophysical means that can be used to probe a planet's interior, seismology remains the most direct. Given that the seismic data gathered on the Moon over 40 years ago revolutionized our understanding of the Moon and are still being used today to produce new insight into the state of the lunar interior, it is no wonder that many future missions, both real and conceptual, plan to take seismometers to other planets. To best facilitate the return of high-quality data from these instruments, as well as to further our understanding of the dynamic processes that modify a planet's interior, various modeling approaches are used to quantify parameters such as the amount and distribution of seismicity, tidal deformation, and seismic structure on and of the terrestrial planets. In addition, recent advances in wavefield modeling have permitted a renewed look at seismic energy transmission and the effects of attenuation and scattering, as well as the presence and effect of a core, on recorded seismograms. In this chapter, we will review these approaches.
Identifying causal variants at loci with multiple signals of association.
Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar
2014-10-01
Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. PMID:25104515
Identifying Causal Variants at Loci with Multiple Signals of Association
Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar
2014-01-01
Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20–50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/. PMID:25104515
Runnqvist, Elin; Bonnard, Mireille; Gauvin, Hanna S; Attarian, Shahram; Trébuchon, Agnès; Hartsuiker, Robert J; Alario, F-Xavier
2016-08-01
Some language processing theories propose that, just as for other somatic actions, self-monitoring of language production is achieved through internal modeling. The cerebellum is the proposed center of such internal modeling in motor control, and the right cerebellum has been linked to an increasing number of language functions, including predictive processing during comprehension. Relating these findings, we tested whether the right posterior cerebellum has a causal role for self-monitoring of speech errors. Participants received 1 Hz repetitive transcranial magnetic stimulation during 15 min to lobules Crus I and II in the right hemisphere, and, in counterbalanced orders, to the contralateral area in the left cerebellar hemisphere (control) in order to induce a temporary inactivation of one of these zones. Immediately afterwards, they engaged in a speech production task priming the production of speech errors. Language production was impaired after right compared to left hemisphere stimulation, a finding that provides evidence for a causal role of the cerebellum during language production. We interpreted this role in terms of internal modeling of upcoming speech through a verbal working memory process used to prevent errors. PMID:27249802
Component Modeling Approach Software Tool
Energy Science and Technology Software Center (ESTSC)
2010-08-23
The Component Modeling Approach Software Tool (CMAST) establishes a set of performance libraries of approved components (frames, glass, and spacer) which can be accessed for configuring fenestration products for a project, and btaining a U-factor, Solar Heat Gain Coefficient (SHGC), and Visible Transmittance (VT) rating for those products, which can then be reflected in a CMA Label Certificate for code compliance. CMAST is web-based as well as client-based. The completed CMA program and software toolmore » will be useful in several ways for a vast array of stakeholders in the industry: Generating performance ratings for bidding projects Ascertaining credible and accurate performance data Obtaining third party certification of overall product performance for code compliance« less
Causal Inference for a Population of Causally Connected Units
van der Laan, Mark J.
2015-01-01
Suppose that we observe a population of causally connected units. On each unit at each time-point on a grid we observe a set of other units the unit is potentially connected with, and a unit-specific longitudinal data structure consisting of baseline and time-dependent covariates, a time-dependent treatment, and a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention. Causal effects of interest are defined as contrasts of the mean of the unit-specific outcomes under different stochastic interventions one wishes to evaluate. This covers a large range of estimation problems from independent units, independent clusters of units, and a single cluster of units in which each unit has a limited number of connections to other units. The allowed dependence includes treatment allocation in response to data on multiple units and so called causal interference as special cases. We present a few motivating classes of examples, propose a structural causal model, define the desired causal quantities, address the identification of these quantities from the observed data, and define maximum likelihood based estimators based on cross-validation. In particular, we present maximum likelihood based super-learning for this network data. Nonetheless, such smoothed/regularized maximum likelihood estimators are not targeted and will thereby be overly bias w.r.t. the target parameter, and, as a consequence, generally not result in asymptotically normally distributed estimators of the statistical target parameter. To formally develop estimation theory, we focus on the simpler case in which the longitudinal data structure is a point-treatment data structure. We formulate a novel targeted maximum likelihood estimator of this estimand and show that the double robustness of the
Inferring causal molecular networks: empirical assessment through a community-based effort
Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach
2016-01-01
Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks. PMID:26901648
2012-01-01
Abstract Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence—synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis—hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider
Recognising discourse causality triggers in the biomedical domain.
Mihăilă, Claudiu; Ananiadou, Sophia
2013-12-01
Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types. PMID:24372037
Causal Inference Based on the Analysis of Events of Relations for Non-stationary Variables.
Yin, Yu; Yao, Dezhong
2016-01-01
The main concept behind causality involves both statistical conditions and temporal relations. However, current approaches to causal inference, focusing on the probability vs. conditional probability contrast, are based on model functions or parametric estimation. These approaches are not appropriate when addressing non-stationary variables. In this work, we propose a causal inference approach based on the analysis of Events of Relations (CER). CER focuses on the temporal delay relation between cause and effect, and a binomial test is established to determine whether an "event of relation" with a non-zero delay is significantly different from one with zero delay. Because CER avoids parameter estimation of non-stationary variables per se, the method can be applied to both stationary and non-stationary signals. PMID:27389921
Causal Inference Based on the Analysis of Events of Relations for Non-stationary Variables
Yin, Yu; Yao, Dezhong
2016-01-01
The main concept behind causality involves both statistical conditions and temporal relations. However, current approaches to causal inference, focusing on the probability vs. conditional probability contrast, are based on model functions or parametric estimation. These approaches are not appropriate when addressing non-stationary variables. In this work, we propose a causal inference approach based on the analysis of Events of Relations (CER). CER focuses on the temporal delay relation between cause and effect, and a binomial test is established to determine whether an “event of relation” with a non-zero delay is significantly different from one with zero delay. Because CER avoids parameter estimation of non-stationary variables per se, the method can be applied to both stationary and non-stationary signals. PMID:27389921
A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.
Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L
2016-03-01
Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. PMID:25902728
Granger-causality maps of diffusion processes
NASA Astrophysics Data System (ADS)
Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A.
2016-02-01
Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.
Granger-causality maps of diffusion processes.
Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A
2016-02-01
Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes. PMID:26986337
Relating Granger causality to long-term causal effects
NASA Astrophysics Data System (ADS)
Smirnov, Dmitry A.; Mokhov, Igor I.
2015-10-01
In estimation of causal couplings between observed processes, it is important to characterize coupling roles at various time scales. The widely used Granger causality reflects short-term effects: it shows how strongly perturbations of a current state of one process affect near future states of another process, and it quantifies that via prediction improvement (PI) in autoregressive models. However, it is often more important to evaluate the effects of coupling on long-term statistics, e.g., to find out how strongly the presence of coupling changes the variance of a driven process as compared to an uncoupled case. No general relationships between Granger causality and such long-term effects are known. Here, we pose the problem of relating these two types of coupling characteristics, and we solve it for a class of stochastic systems. Namely, for overdamped linear oscillators, we rigorously derive that the above long-term effect is proportional to the short-term effects, with the proportionality coefficient depending on the prediction interval and relaxation times. We reveal that this coefficient is typically considerably greater than unity so that small normalized PI values may well correspond to quite large long-term effects of coupling. The applicability of the derived relationship to wider classes of systems, its limitations, and its value for further research are discussed. To give a real-world example, we analyze couplings between large-scale climatic processes related to sea surface temperature variations in equatorial Pacific and North Atlantic regions.
Zigler, Corwin M; Dominici, Francesca; Wang, Yun
2012-04-01
Methods for causal inference regarding health effects of air quality regulations are met with unique challenges because (1) changes in air quality are intermediates on the causal pathway between regulation and health, (2) regulations typically affect multiple pollutants on the causal pathway towards health, and (3) regulating a given location can affect pollution at other locations, that is, there is interference between observations. We propose a principal stratification method designed to examine causal effects of a regulation on health that are and are not associated with causal effects of the regulation on air quality. A novel feature of our approach is the accommodation of a continuously scaled multivariate intermediate response vector representing multiple pollutants. Furthermore, we use a spatial hierarchical model for potential pollution concentrations and ultimately use estimates from this model to assess validity of assumptions regarding interference. We apply our method to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor. PMID:22267524
Padula, Amy M; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B
2012-11-01
Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000-2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants. PMID:23045474
Padula, Amy M.; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B.
2012-01-01
Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000–2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants. PMID:23045474
Dimensional Reduction in Causal Set Gravity
NASA Astrophysics Data System (ADS)
Carlip, Steven
2016-03-01
Several different approaches to quantum gravity indicate that the effective dimension of spacetime falls to approximately two at very short distances. I provide evidence for similar behavior in the causal set approach to quantum gravity. Supported in part by Department of Energy Grant DE-FG02-91ER40674.
Causality discovery technology
NASA Astrophysics Data System (ADS)
Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.
2012-11-01
Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.
Causal conditionals and counterfactuals
Frosch, Caren A.; Byrne, Ruth M.J.
2012-01-01
Causal counterfactuals e.g., ‘if the ignition key had been turned then the car would have started’ and causal conditionals e.g., ‘if the ignition key was turned then the car started’ are understood by thinking about multiple possibilities of different sorts, as shown in six experiments using converging evidence from three different types of measures. Experiments 1a and 1b showed that conditionals that comprise enabling causes, e.g., ‘if the ignition key was turned then the car started’ primed people to read quickly conjunctions referring to the possibility of the enabler occurring without the outcome, e.g., ‘the ignition key was turned and the car did not start’. Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have). Experiments 3a and 3b showed that people made different inferences from counterfactuals presented with enabling conditions compared to none. The implications of the results for alternative theories of conditionals are discussed. PMID:22858874
Agent-based modeling: a new approach for theory building in social psychology.
Smith, Eliot R; Conrey, Frederica R
2007-02-01
Most social and psychological phenomena occur not as the result of isolated decisions by individuals but rather as the result of repeated interactions between multiple individuals over time. Yet the theory-building and modeling techniques most commonly used in social psychology are less than ideal for understanding such dynamic and interactive processes. This article describes an alternative approach to theory building, agent-based modeling (ABM), which involves simulation of large numbers of autonomous agents that interact with each other and with a simulated environment and the observation of emergent patterns from their interactions. The authors believe that the ABM approach is better able than prevailing approaches in the field, variable-based modeling (VBM) techniques such as causal modeling, to capture types of complex, dynamic, interactive processes so important in the social world. The article elaborates several important contrasts between ABM and VBM and offers specific recommendations for learning more and applying the ABM approach. PMID:18453457
Johnson, Samuel G. B.; Ahn, Woo-kyoung
2014-01-01
Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we used causal transitivity—the inference given A causes B and B causes C that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links (Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4–5). PMID:25556901
Lightweight causal and atomic group multicast
NASA Technical Reports Server (NTRS)
Birman, Kenneth P.; Schiper, Andre; Stephenson, Pat
1991-01-01
The ISIS toolkit is a distributed programming environment based on support for virtually synchronous process groups and group communication. A suite of protocols is presented to support this model. The approach revolves around a multicast primitive, called CBCAST, which implements a fault-tolerant, causally ordered message delivery. This primitive can be used directly or extended into a totally ordered multicast primitive, called ABCAST. It normally delivers messages immediately upon reception, and imposes a space overhead proportional to the size of the groups to which the sender belongs, usually a small number. It is concluded that process groups and group communication can achieve performance and scaling comparable to that of a raw message transport layer. This finding contradicts the widespread concern that this style of distributed computing may be unacceptably costly.
In silico model-based inference: a contemporary approach for hypothesis testing in network biology
Klinke, David J.
2014-01-01
Inductive inference plays a central role in the study of biological systems where one aims to increase their understanding of the system by reasoning backwards from uncertain observations to identify causal relationships among components of the system. These causal relationships are postulated from prior knowledge as a hypothesis or simply a model. Experiments are designed to test the model. Inferential statistics are used to establish a level of confidence in how well our postulated model explains the acquired data. This iterative process, commonly referred to as the scientific method, either improves our confidence in a model or suggests that we revisit our prior knowledge to develop a new model. Advances in technology impact how we use prior knowledge and data to formulate models of biological networks and how we observe cellular behavior. However, the approach for model-based inference has remained largely unchanged since Fisher, Neyman and Pearson developed the ideas in the early 1900’s that gave rise to what is now known as classical statistical hypothesis (model) testing. Here, I will summarize conventional methods for model-based inference and suggest a contemporary approach to aid in our quest to discover how cells dynamically interpret and transmit information for therapeutic aims that integrates ideas drawn from high performance computing, Bayesian statistics, and chemical kinetics. PMID:25139179
Confounding Effects of Phase Delays on Causality Estimation
Vakorin, Vasily A.; Mišić, Bratislav; Krakovska, Olga; Bezgin, Gleb; McIntosh, Anthony R.
2013-01-01
Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature. Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies at which the signals become synchronized to each other. We found that an information-theoretic approach, which takes into account different time lags between the past of one signal and the future of another signal, was the most robust to phase effects. PMID:23349720
Dhakal, K; Tiezzi, F; Clay, J S; Maltecca, C
2015-04-01
Health disorders in dairy cows have a substantial effect on the profitability of a dairy enterprise because of loss in milk sales, culling of unhealthy cows, and replacement costs. Complex relationships exist between health disorders and production traits. Understanding the causal structures among these traits may help us disentangle these complex relationships. The principal objective of this study was to use producer-recorded data to explore phenotypic and genetic relationships among reproductive and metabolic health disorders and production traits in first-lactation US Holsteins. A total of 77,004 first-lactation daughters' records of 2,183 sires were analyzed using recursive models. Health data contained information on reproductive health disorders [retained placenta (RP); metritis (METR)] and metabolic health disorders [ketosis (KETO); displaced abomasum (DA)]. Production traits included mean milk yield (MY) from early lactation (mean MY from 6 to 60 d in milk and from 61 to 120 d in milk), peak milk yield (PMY), day in milk of peak milk yield (PeakD), and lactation persistency (LP). Three different sets of traits were analyzed in which recursive effects from each health disorder on culling, recursive effects of one health disorder on another health disorder and on MY, and recursive effects of each health disorder on production traits, including PeakD, PMY, and LP, were assumed. Different recursive Gaussian-threshold and threshold models were implemented in a Bayesian framework. Estimates of the structural coefficients obtained between health disorders and culling were positive; on the liability scale, the structural coefficients ranged from 0.929 to 1.590, confirming that the presence of a health disorder increased culling. Positive recursive effects of RP to METR (0.117) and of KETO to DA (0.122) were estimated, whereas recursive effects from health disorders to production traits were negligible in all cases. Heritability estimates of health disorders ranged
Approaches for modeling magnetic nanoparticle dynamics
Reeves, Daniel B; Weaver, John B
2014-01-01
Magnetic nanoparticles are useful biological probes as well as therapeutic agents. There have been several approaches used to model nanoparticle magnetization dynamics for both Brownian as well as Néel rotation. The magnetizations are often of interest and can be compared with experimental results. Here we summarize these approaches including the Stoner-Wohlfarth approach, and stochastic approaches including thermal fluctuations. Non-equilibrium related temperature effects can be described by a distribution function approach (Fokker-Planck equation) or a stochastic differential equation (Langevin equation). Approximate models in several regimes can be derived from these general approaches to simplify implementation. PMID:25271360
Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David
2014-01-01
Granger causality (GC) is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length τ, i.e., the GC value is a function of τ. Using the GC analysis for the topology extraction of the simplest integrate-and-fire neuronal network of two neurons, we discuss behaviors of the GC value as a function of τ, which exhibits (i) oscillations, often vanishing at certain finite sampling interval lengths, (ii) the GC vanishes linearly as one uses finer and finer sampling. We show that these sampling effects can occur in both linear and non-linear dynamics: the GC value may vanish in the presence of true causal influence or become non-zero in the absence of causal influence. Without properly taking this issue into account, GC analysis may produce unreliable conclusions about causal influence when applied to empirical data. These sampling artifacts on the GC value greatly complicate the reliability of causal inference using the GC analysis, in general, and the validity of topology reconstruction for networks, in particular. We use idealized linear models to illustrate possible mechanisms underlying these phenomena and to gain insight into the general spectral structures that give rise to these sampling effects. Finally, we present an approach to circumvent these sampling artifacts to obtain reliable GC values. PMID:25126067
On a renormalization group scheme for causal dynamical triangulations
NASA Astrophysics Data System (ADS)
Cooperman, Joshua H.
2016-03-01
The causal dynamical triangulations approach aims to construct a quantum theory of gravity as the continuum limit of a lattice-regularized model of dynamical geometry. A renormalization group scheme—in concert with finite size scaling analysis—is essential to this aim. Formulating and implementing such a scheme in the present context raises novel and notable conceptual and technical problems. I explored these problems, and, building on standard techniques, suggested potential solutions in a previous paper (Cooperman, arXiv:gr-qc/1410.0026). As an application of these solutions, I now propose a renormalization group scheme for causal dynamical triangulations. This scheme differs significantly from that studied recently by Ambjørn, Görlich, Jurkiewicz, Kreienbuehl, and Loll.
Causal inference and the data-fusion problem.
Bareinboim, Elias; Pearl, Judea
2016-07-01
We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion-piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks. PMID:27382148
Causal inference and the data-fusion problem
Bareinboim, Elias; Pearl, Judea
2016-01-01
We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion—piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to queries of interest. The availability of multiple heterogeneous datasets presents new opportunities to big data analysts, because the knowledge that can be acquired from combined data would not be possible from any individual source alone. However, the biases that emerge in heterogeneous environments require new analytical tools. Some of these biases, including confounding, sampling selection, and cross-population biases, have been addressed in isolation, largely in restricted parametric models. We here present a general, nonparametric framework for handling these biases and, ultimately, a theoretical solution to the problem of data fusion in causal inference tasks. PMID:27382148
Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems
Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David
2014-01-01
Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (IF) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based IF neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings. PMID:24586285
Causality: Physics and Philosophy
ERIC Educational Resources Information Center
Chatterjee, Atanu
2013-01-01
Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…
ERIC Educational Resources Information Center
White, Peter A.
2006-01-01
It is hypothesized that there is a pervasive and fundamental bias in humans' understanding of physical causation: Once the roles of cause and effect are assigned to objects in interactions, people tend to overestimate the strength and importance of the causal object and underestimate that of the effect object in bringing about the outcome. This…
Ahn, Woo-kyoung; Taylor, Eric G; Kato, Daniel; Marsh, Jessecae K; Bloom, Paul
2013-06-01
The current study examines causal essentialism, derived from psychological essentialism of concepts. We examine whether people believe that members of a category share some underlying essence that is both necessary and sufficient for category membership and that also causes surface features. The main claim is that causal essentialism is restricted to categories that correspond to our intuitive notions of existing kinds and hence is more attenuated for categories that are based on arbitrary criteria. Experiments 1 and 3 found that people overtly endorse causal essences in nonarbitrary kinds but are less likely to do so for arbitrary categories. Experiments 2 and 4 found that people were more willing to generalize a member's known causal relations (or lack thereof) when dealing with a kind than when dealing with an arbitrary category. These differences between kinds and arbitrary categories were found across various domains-not only for categories of living things, but also for artefacts. These findings have certain real-world implications, including how people make sense of mental disorders that are treated as real kinds. PMID:23098315
Causal Responsibility and Counterfactuals
ERIC Educational Resources Information Center
Lagnado, David A.; Gerstenberg, Tobias; Zultan, Ro'i
2013-01-01
How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in…
Causal Mediation Analyses for Randomized Trials.
Lynch, Kevin G; Cary, Mark; Gallop, Robert; Ten Have, Thomas R
2008-01-01
In the context of randomized intervention trials, we describe causal methods for analyzing how post-randomization factors constitute the process through which randomized baseline interventions act on outcomes. Traditionally, such mediation analyses have been undertaken with great caution, because they assume that the mediating factor is also randomly assigned to individuals in addition to the randomized baseline intervention (i.e., sequential ignorability). Because the mediating factors are typically not randomized, such analyses are unprotected from unmeasured confounders that may lead to biased inference. We review several causal approaches that attempt to reduce such bias without assuming that the mediating factor is randomized. However, these causal approaches require certain interaction assumptions that may be assessed if there is enough treatment heterogeneity with respect to the mediator. We describe available estimation procedures in the context of several examples from the literature and provide resources for software code. PMID:19484136
Understanding Complexity: Pattern Recognitions, Emergent Phenomena and Causal Coupling
NASA Astrophysics Data System (ADS)
Raia, F.
2010-12-01
In teaching and learning complex systems we face a fundamental issue: Simultaneity of causal interactions -where effects are at the same time causes of systems’ behavior. Complex systems’ behavior and evolution are controlled by negative and positive feedback processes, continually changing boundary conditions and complex interaction between systems levels (emergence). These processes cannot be described and understood in a mechanistic framework where causality is conceived of being mostly of cause-effect nature or a linear chain of causes and effects. Mechanist causality by definition is characterized by the assumption that an earlier phenomenon A has a causal effect on the development of a phenomenon B. Since this concept also assumes unidirectional time, B cannot have an effect on A. Since students study science mostly in the lingering mechanistic framework, they have problems understanding complex systems. Specifically, our research on students understanding of complexity indicates that our students seem to have great difficulties in explaining mechanisms underlying natural processes within the current paradigm. Students tend to utilize simple linear model of causality and establish a one-to-one correspondence between cause and effect describing phenomena such as emergence and self-organization as being mechanistically caused. Contrary to experts, when presented with data distribution -spatial and/or temporal-, students first consider or search for a unique cause without describing the distribution or a recognized pattern. Our research suggests that students do not consider a pattern observed as an emergent phenomenon and therefore a causal determinant influencing and controlling the evolution of the system. Changes in reasoning have been observed when students 1) are iteratively asked to recognize and describe patterns in data distribution and 2) subsequently learn to identify these patterns as emergent phenomena and as fundamental causal controls over
Causal diagrams in systems epidemiology
2012-01-01
Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback. PMID:22429606
Ahmadvand, Mostafa Karami, Ezatollah
2009-02-15
The purpose of this study was to explore the social impacts of the floodwater spreading project (FWSP) on the Gareh-Bygone plain, Iran. The study was in the form of a causal comparative design, and a triangulation technique was used to collect data including the use of survey data, archival data, and a participatory rural appraisal (PRA). The causal comparative method requires a comparison of villages with and without the FWSP. Therefore, a survey was conducted using stratified random sampling to select 202 households in villages with and without FWSP in the plain. Significant differences were found between the respondents in villages with and without FWSP with regard to social impact criteria. In spite of the project had negative impact on perceived wellbeing, social capital, social structure development; it had positive impact on quality of life, rural and agricultural economic conditions, and conservation of community resources. However, no significant difference was found between women and men regarding the SIA of FWSP in Gareh-Bygone plain. Analysis of the archival data and PRA techniques supported the survey results and demonstrated that the project improved environmental criteria and deteriorated social dimensions.
Causality violation, gravitational shockwaves and UV completion
NASA Astrophysics Data System (ADS)
Hollowood, Timothy J.; Shore, Graham M.
2016-03-01
The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of "time machines", i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.
Transitive reasoning distorts induction in causal chains.
von Sydow, Momme; Hagmayer, York; Meder, Björn
2016-04-01
A probabilistic causal chain A→B→C may intuitively appear to be transitive: If A probabilistically causes B, and B probabilistically causes C, A probabilistically causes C. However, probabilistic causal relations can only guaranteed to be transitive if the so-called Markov condition holds. In two experiments, we examined how people make probabilistic judgments about indirect relationships A→C in causal chains A→B→C that violate the Markov condition. We hypothesized that participants would make transitive inferences in accordance with the Markov condition although they were presented with counterevidence showing intransitive data. For instance, participants were successively presented with data entailing positive dependencies A→B and B→C. At the same time, the data entailed that A and C were statistically independent. The results of two experiments show that transitive reasoning via a mediating event B influenced and distorted the induction of the indirect relation between A and C. Participants' judgments were affected by an interaction of transitive, causal-model-based inferences and the observed data. Our findings support the idea that people tend to chain individual causal relations into mental causal chains that obey the Markov condition and thus allow for transitive reasoning, even if the observed data entail that such inferences are not warranted. PMID:26620811
Causal Status and Coherence in Causal-Based Categorization
ERIC Educational Resources Information Center
Rehder, Bob; Kim, ShinWoo
2010-01-01
Research has documented two effects of interfeature causal knowledge on classification. A "causal status effect" occurs when features that are causes are more important to category membership than their effects. A "coherence effect" occurs when combinations of features that are consistent with causal laws provide additional evidence of category…
Xu, Fang-Fang; Han, Lu; He, Hong-Jian; Zhu, Yi-Hong; Zhong, Jian-Hui
2016-06-25
The effective connectivity of default mode network (DMN) and its change after taking methylphenidate (MPH) were investigated in this study based on resting-state functional magnetic resonance imaging. Dynamic causal modeling (DCM) was applied to compare the effective connectivity between the conditions of taking MPH and placebo for 18 healthy male volunteers. Started with the network structural basis provided by a recent literature, endogenous low frequency fluctuation signals (0.01-0.08 Hz) of each node of DMN were taken as the driving input, and thirty-two possible models were designed according to the modulation effect of MPH on different connections between nodes. Model fitting and Bayesian model selection were performed to find the winning model and corresponding parameters. Our results indicated that the effective connectivity from medial prefrontal cortex (MPFC) to posterior cingulated cortex (PCC), from left/right inferior parietal lobule (L/RIPL) to MPFC, and from RIPL to PCC were excitatory, whereas the connectivity from LIPL to PCC was inhibitory. Further t-test statistics on connectivity parameters found that MPH significantly reduced the link from RIPL to MPFC in DMN (t = 2.724, P = 0.016) and changed the weak excitatory state to inhibitory state. However, it had no significant effect on other connections. In all, our results demonstrated that MPH modulates the effective connectivity within DMN in resting state. PMID:27350198
2013-01-01
Background Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action. Results We present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mechanism to downstream measurable quantities. These small directed networks are generated from a knowledge base of literature-curated qualitative biological cause-and-effect relationships expressed as a network. The small mechanism networks are evaluated as hypotheses to explain observed differential measurements. We provide a simple implementation of this methodology, Whistle, specifically geared towards the analysis of gene expression data and using prior knowledge expressed in Biological Expression Language (BEL). We present the Whistle analyses for three transcriptomic data sets using a publically available knowledge base. The mechanisms inferred by Whistle are consistent with the expected biology for each data set. Conclusions Reverse Causal Reasoning yields mechanistic insights to the interpretation of gene expression profiling data that are distinct from and complementary to the results of analyses using ontology or pathway gene sets. This reverse engineering algorithm provides an evidence-driven approach to the development of models of disease, drug action, and drug toxicity. PMID:24266983
A Semantic Modeling Approach to Metadata.
ERIC Educational Resources Information Center
Brasethvik, Terje
1998-01-01
Explores problems in information sharing; discusses the concept of metadata; illustrates its use on the World Wide Web, as well as other related approaches; and presents an approach to information sharing that uses a semantic modeling language (referent model language) as the basis for expressing semantics of information and designing metadata…
Model of Conceptual Change for INQPRO: A Bayesian Network Approach
ERIC Educational Resources Information Center
Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn
2013-01-01
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Model compilation: An approach to automated model derivation
NASA Technical Reports Server (NTRS)
Keller, Richard M.; Baudin, Catherine; Iwasaki, Yumi; Nayak, Pandurang; Tanaka, Kazuo
1990-01-01
An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration.
Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables
NASA Astrophysics Data System (ADS)
Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.
2009-12-01
Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.
Causal Cohesion and Story Coherence.
ERIC Educational Resources Information Center
Trabasso, Tom; And Others
Based on the theory that a story's coherence depends directly on the causal cohesiveness of the story's individual events, this paper describes (1) a process by which readers use causal reasoning to connect events, (2) what memory representations result from this reasoning, and (3) the implications of test data on causal reasoning. Following a…
THE CHILD'S CONCEPTION OF PHYSICAL CAUSALITY.
ERIC Educational Resources Information Center
PIAGET, JEAN
THE CHILD'S CONCEPTION OF PHYSICAL CAUSALITY WAS INVESTIGATED. THREE METHODS OF INVESTIGATION WERE USED. THE FIRST METHOD WAS PURELY VERBAL, AND CONSISTED OF A SERIES OF QUESTIONS DIRECTED TO CHILDREN, REGARDING SOME NATURAL PHENOMENON. THE SECOND METHOD INVOLVED A HALF-VERBAL, HALF-PRACTICAL APPROACH, WHEREIN A SPECIFIC REFERENCE TO NATURAL…
Measuring frequency domain granger causality for multiple blocks of interacting time series.
Faes, Luca; Nollo, Giandomenico
2013-04-01
In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing measures to the analysis of multiple blocks of time series. Specifically, the block DC (bDC) and block PDC (bPDC) extend DC and PDC to vector-valued processes, while their logarithmic counterparts, denoted as multivariate total feedback [Formula: see text] and direct feedback [Formula: see text], represent into a full multivariate framework the Geweke's measures. Theoretical analysis of the proposed measures shows that they: (i) possess desirable properties of causality measures; (ii) are able to reflect either direct causality (bPDC, [Formula: see text] or total (direct + indirect) causality (bDC, [Formula: see text] between time series blocks; (iii) reduce to the DC and PDC measures for scalar-valued processes, and to the Geweke's measures for pairs of processes; (iv) are able to capture internal dependencies between the scalar constituents of the analyzed vector processes. Numerical analysis showed that the proposed measures can be efficiently estimated from short time series, allow to represent in an objective, compact way the information derived from the causal analysis of several pairs of time series, and may detect frequency domain causality more accurately than existing measures. The proposed measures find their natural application in the evaluation of directional
A new approach to turbulence modeling
NASA Technical Reports Server (NTRS)
Perot, B.; Moin, P.
1996-01-01
A new approach to Reynolds averaged turbulence modeling is proposed which has a computational cost comparable to two equation models but a predictive capability approaching that of Reynolds stress transport models. This approach isolates the crucial information contained within the Reynolds stress tensor, and solves transport equations only for a set of 'reduced' variables. In this work, Direct Numerical Simulation (DNS) data is used to analyze the nature of these newly proposed turbulence quantities and the source terms which appear in their respective transport equations. The physical relevance of these quantities is discussed and some initial modeling results for turbulent channel flow are presented.
Musgrove, Donald R; Eberly, Lynn E; Klimes-Dougan, Bonnie; Basgoze, Zeynep; Thomas, Kathleen M; Mueller, Bryon A; Houri, Alaa; Lim, Kelvin O; Cullen, Kathryn R
2015-12-01
Major depressive disorder (MDD) is a significant contributor to lifetime disability and frequently emerges in adolescence, yet little is known about the neural mechanisms of MDD in adolescents. Dynamic causal modeling (DCM) analysis is an innovative tool that can shed light on neural network abnormalities. A DCM analysis was conducted to test several frontolimbic effective connectivity models in 27 adolescents with MDD and 21 healthy adolescents. The best neural model for each person was identified using Bayesian model selection. The findings revealed that the two adolescent groups fit similar optimal neural models. The best across-groups model was then used to infer upon both within-group and between-group tests of intrinsic and modulation parameters of the network connections. First, for model validation, within-group tests revealed robust evidence for bottom-up connectivity, but less evidence for strong top-down connectivity in both groups. Second, we tested for differences between groups on the validated parameters of the best model. This revealed that adolescents with MDD had significantly weaker bottom-up connectivity in one pathway, from amygdala to sgACC (p=0.008), than healthy controls. This study provides the first examination of effective connectivity using DCM within neural circuitry implicated in emotion processing in adolescents with MDD. These findings aid in advancing understanding the neurobiology of early-onset MDD during adolescence and have implications for future research investigating how effective connectivity changes across contexts, with development, over the course of the disease, and after intervention. PMID:26050933
ERIC Educational Resources Information Center
Lefevre, Pierre; de Suremain, Charles-Edouard; Rubin de Celis, Emma; Sejas, Edgar
2004-01-01
The paper discusses the utility of constructing causal models in focus groups. This was experienced as a complement to an in-depth ethnographic research on the differing perceptions of caretakers and health professionals on child's growth and development in Peru and Bolivia. The rational, advantages, difficulties and necessary adaptations of…
A Unified Approach to Modeling Multidisciplinary Interactions
NASA Technical Reports Server (NTRS)
Samareh, Jamshid A.; Bhatia, Kumar G.
2000-01-01
There are a number of existing methods to transfer information among various disciplines. For a multidisciplinary application with n disciplines, the traditional methods may be required to model (n(exp 2) - n) interactions. This paper presents a unified three-dimensional approach that reduces the number of interactions from (n(exp 2) - n) to 2n by using a computer-aided design model. The proposed modeling approach unifies the interactions among various disciplines. The approach is independent of specific discipline implementation, and a number of existing methods can be reformulated in the context of the proposed unified approach. This paper provides an overview of the proposed unified approach and reformulations for two existing methods. The unified approach is specially tailored for application environments where the geometry is created and managed through a computer-aided design system. Results are presented for a blended-wing body and a high-speed civil transport.
Nordbø, Øyvind; Gjuvsland, Arne B.; Nermoen, Anders; Land, Sander; Niederer, Steven; Lamata, Pablo; Lee, Jack; Smith, Nicolas P.; Omholt, Stig W.; Vik, Jon Olav
2015-01-01
A scientific understanding of individual variation is key to personalized medicine, integrating genotypic and phenotypic information via computational physiology. Genetic effects are often context-dependent, differing between genetic backgrounds or physiological states such as disease. Here, we analyse in silico genotype–phenotype maps (GP map) for a soft-tissue mechanics model of the passive inflation phase of the heartbeat, contrasting the effects of microstructural and other low-level parameters assumed to be genetically influenced, under normal, concentrically hypertrophic and eccentrically hypertrophic geometries. For a large number of parameter scenarios, representing mock genetic variation in low-level parameters, we computed phenotypes describing the deformation of the heart during inflation. The GP map was characterized by variance decompositions for each phenotype with respect to each parameter. As hypothesized, the concentric geometry allowed more low-level parameters to contribute to variation in shape phenotypes. In addition, the relative importance of overall stiffness and fibre stiffness differed between geometries. Otherwise, the GP map was largely similar for the different heart geometries, with little genetic interaction between the parameters included in this study. We argue that personalized medicine can benefit from a combination of causally cohesive genotype–phenotype modelling, and strategic phenotyping that captures effect modifiers not explicitly included in the mechanistic model. PMID:25833237
Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry
2013-01-01
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258
The Newtonian approximation in Causal Dynamical Triangulations
NASA Astrophysics Data System (ADS)
Getchell, Adam
2015-04-01
I review how to derive Newton's law of universal gravitation from the Weyl strut between two Chazy-Curzon particles. I also briefly review Causal Dynamical Triangulations (CDT), a method for evaluating the path integral from canonical quantum gravity using Regge calculus and restrictions of the class of simplicial manifolds evaluated to those with a defined time foliation, thus enforcing a causal structure. I then discuss how to apply this approach to Causal Dynamical Triangulations, in particular modifying the algorithm to keep two simplicial submanifolds with curvature (i.e. mass) a fixed distance from each other, modulo regularized deviations and across all time slices. I then discuss how to determine if CDT produces an equivalent Weyl strut, which can then be used to obtain the Newtonian limit. I wrap up with a brief discussion of computational methods and code development.
Matrix model approach to cosmology
NASA Astrophysics Data System (ADS)
Chaney, A.; Lu, Lei; Stern, A.
2016-03-01
We perform a systematic search for rotationally invariant cosmological solutions to toy matrix models. These models correspond to the bosonic sector of Lorentzian Ishibashi, Kawai, Kitazawa and Tsuchiya (IKKT)-type matrix models in dimensions d less than ten, specifically d =3 and d =5 . After taking a continuum (or commutative) limit they yield d -1 dimensional Poisson manifolds. The manifolds have a Lorentzian induced metric which can be associated with closed, open, or static space-times. For d =3 , we obtain recursion relations from which it is possible to generate rotationally invariant matrix solutions which yield open universes in the continuum limit. Specific examples of matrix solutions have also been found which are associated with closed and static two-dimensional space-times in the continuum limit. The solutions provide for a resolution of cosmological singularities, at least within the context of the toy matrix models. The commutative limit reveals other desirable features, such as a solution describing a smooth transition from an initial inflation to a noninflationary era. Many of the d =3 solutions have analogues in higher dimensions. The case of d =5 , in particular, has the potential for yielding realistic four-dimensional cosmologies in the continuum limit. We find four-dimensional de Sitter d S4 or anti-de Sitter AdS4 solutions when a totally antisymmetric term is included in the matrix action. A nontrivial Poisson structure is attached to these manifolds which represents the lowest order effect of noncommutativity. For the case of AdS4 , we find one particular limit where the lowest order noncommutativity vanishes at the boundary, but not in the interior.
HABITAT MODELING APPROACHES FOR RESTORATION SITE SELECTION
Numerous modeling approaches have been used to develop predictive models of species-environment and species-habitat relationships. These models have been used in conservation biology and habitat or species management, but their application to restoration efforts has been minimal...
An Instructional Approach to Modeling in Microevolution.
ERIC Educational Resources Information Center
Thompson, Steven R.
1988-01-01
Describes an approach to teaching population genetics and evolution and some of the ways models can be used to enhance understanding of the processes being studied. Discusses the instructional plan, and the use of models including utility programs and analysis with models. Provided are a basic program and sample program outputs. (CW)
The use of canine models of inherited retinal degeneration to test novel therapeutic approaches
Beltran, William A.
2011-01-01
Inherited retinal degenerations (RDs) are a common cause of blindness in dogs and in humans. Over the past two decades numerous genes causally associated with these diseases have been identified and several canine models have been used to improve our understanding of the molecular mechanisms of RDs, as well as to test the proof of principle and safety of novel therapies. This review briefly summarizes the drug delivery approaches and therapeutic strategies that have been and are currently tested in dogs, with a particular emphasis on corrective gene therapy, and retinal neuroprotection. PMID:19392879
Causal simulation and sensor planning in predictive monitoring
NASA Technical Reports Server (NTRS)
Doyle, Richard J.
1989-01-01
Two issues are addressed which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. The approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, useful in assessing the relative importance of events so that sensor resources can be allocated effectively. The potential applicability of this work to the execution monitoring of robot task plans is briefly discussed.
Chemogenetic approach to model hypofrontality.
Peña, Ike Dela; Shi, Wei-Xing
2016-08-01
Clinical evidence suggests that the prefrontal cortex (PFC) is hypofunctional in disorders including schizophrenia, drug addiction, and attention-deficit/hyperactivity disorder (ADHD). In schizophrenia, hypofrontality has been further suggested to cause both the negative and cognitive symptoms, and overactivity of dopamine neurons that project to subcortical areas. The latter may contribute to the development of positive symptoms of the disorder. Nevertheless, what causes hypofrontality and how it alters dopamine transmission in subcortical structures remain unclear due, in part, to the difficulty in modeling hypofrontality using previous techniques (e.g. PFC lesioning, focal cooling, repeated treatment with psychotomimetic drugs). We propose that the use of designer receptors exclusively activated by designer drugs (DREADDs) chemogenetic technique will allow precise interrogations of PFC functions. Combined with electrophysiological recordings, we can investigate the effects of PFC hypofunction on activity of dopamine neurons. Importantly, from a drug target discovery perspective, the use of DREADDs will enable us to examine whether chemogenetically enhancing PFC activity will reverse the behavioral abnormalities associated with PFC hypofunction and dopamine neuron overactivity, and also explore druggable targets for the treatment of schizophrenia and other disorders associated with abnormalities via modulation of the G-protein coupled receptor signaling pathway. In conclusion, the use of the DREADDs technique has several advantages over other previously employed strategies to simulate PFC hypofunction not only in terms of disease modeling but also from the viewpoint of drug target discovery. PMID:27372868
Uniqueness of place: uniqueness of models. The FLEX modelling approach
NASA Astrophysics Data System (ADS)
Fenicia, F.; Savenije, H. H. G.; Wrede, S.; Schoups, G.; Pfister, L.
2009-04-01
The current practice in hydrological modelling is to make use of model structures that are fixed and a-priori defined. However, for a model to reflect uniqueness of place while maintaining parsimony, it is necessary to be flexible in its architecture. We have developed a new approach for the development and testing of hydrological models, named the FLEX approach. This approach allows the formulation of alternative model structures that vary in configuration and complexity, and uses an objective method for testing and comparing model performance. We have tested this approach on three headwater catchments in Luxembourg with marked differences in hydrological response, where we have generated 15 alternative model structures. Each of the three catchments is best represented by a different model architecture. Our results clearly show that uniqueness of place necessarily leads to uniqueness of models.
Challenges in structural approaches to cell modeling.
Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A
2016-07-31
Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. PMID:27255863
ERIC Educational Resources Information Center
Graham, Carroll M.; Scott, Aaron J.; Nafukho, Fredrick M.
2008-01-01
While theoretical models aimed at explaining or predicting employee turnover outcomes have been developed, minimal consideration has been given to the same task regarding safety, often measured as the probability of a crash in a given time frame. The present literature review identifies four constructs from turnover literature, which are believed…
ERIC Educational Resources Information Center
Peltier, James W.; Schibrowsky, John A.; Drago, William
2007-01-01
A structural model of the drivers of online education is proposed and tested. The findings help to identify the interrelated nature of the lectures delivered via technology outside of the traditional classroom, the importance of mentoring, the need to develop course structure, the changing roles for instructors and students, and the importance of…
ERIC Educational Resources Information Center
Hagedorn, Linda Serra
1996-01-01
Using data from a national survey of faculty, a study examined the role of male/female wage differentials in a model of job satisfaction for full-time female faculty. Results indicated that as gender-based wage differentials increased, females' global job satisfaction decreased, with the effect mainly in faculty perceptions of the institution.…
ERIC Educational Resources Information Center
Hammen, Constance; And Others
1990-01-01
Assessed 8- to 16-year-olds of 64 mothers with recurrent unipolar or bipolar affective disorders, chronic medical illness, or no disorder. Results supported a model in which children's outcomes 6 months later were caused by maternal functioning and characteristics of the child. (RH)
A Hierarchical Systems Approach to Model Validation
NASA Astrophysics Data System (ADS)
Easterbrook, S. M.
2011-12-01
Existing approaches to the question of how climate models should be evaluated tend to rely on either philosophical arguments about the status of models as scientific tools, or on empirical arguments about how well runs from a given model match observational data. These have led to quantitative approaches expressed in terms of model bias or forecast skill, and ensemble approaches where models are assessed according to the extent to which the ensemble brackets the observational data. Unfortunately, such approaches focus the evaluation on models per se (or more specifically, on the simulation runs they produce) as though the models can be isolated from their context. Such approach may overlook a number of important aspects of the use of climate models: - the process by which models are selected and configured for a given scientific question. - the process by which model outputs are selected, aggregated and interpreted by a community of expertise in climatology. - the software fidelity of the models (i.e. whether the running code is actually doing what the modellers think it's doing). - the (often convoluted) history that begat a given model, along with the modelling choices long embedded in the code. - variability in the scientific maturity of different model components within a coupled system. These omissions mean that quantitative approaches cannot assess whether a model produces the right results for the wrong reasons, or conversely, the wrong results for the right reasons (where, say the observational data is problematic, or the model is configured to be unlike the earth system for a specific reason). Hence, we argue that it is a mistake to think that validation is a post-hoc process to be applied to an individual "finished" model, to ensure it meets some criteria for fidelity to the real world. We are therefore developing a framework for model validation that extends current approaches down into the detailed codebase and the processes by which the code is built
Quantum information causality.
Pitalúa-García, Damián
2013-05-24
How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs. PMID:23745844
An approach to solving large reliability models
NASA Technical Reports Server (NTRS)
Boyd, Mark A.; Veeraraghavan, Malathi; Dugan, Joanne Bechta; Trivedi, Kishor S.
1988-01-01
This paper describes a unified approach to the problem of solving large realistic reliability models. The methodology integrates behavioral decomposition, state trunction, and efficient sparse matrix-based numerical methods. The use of fault trees, together with ancillary information regarding dependencies to automatically generate the underlying Markov model state space is proposed. The effectiveness of this approach is illustrated by modeling a state-of-the-art flight control system and a multiprocessor system. Nonexponential distributions for times to failure of components are assumed in the latter example. The modeling tool used for most of this analysis is HARP (the Hybrid Automated Reliability Predictor).
A MIMIC approach to modeling the underground economy in Taiwan
NASA Astrophysics Data System (ADS)
Wang, David Han-Min; Lin, Jer-Yan; Yu, Tiffany Hui-Kuang
2006-11-01
The size of underground economy (UE) expansion usually increases the tax gap, impose a burden on the economy, and results in tax distortions. This study uses the MIMIC approach to model the causal variables and indicating variables to estimate the UE in Taiwan. We also focus on testing the data for non-stationarity and perform diagnostic tests. By using annual time-series data for Taiwan from 1961 to 2003, it is found that the estimated size of the UE varies from 11.0% to 13.1% before 1988, and from 10.6% to 11.8% from 1989 onwards. That the size of the UE experienced a substantial downward shift in 1989 indicates that there was a structural break. The UE is significantly and positively affected by such casual variables as the logarithm of real government consumption and currency inflation, but is negatively affected by the tax burden at 5% significant level. Unemployment rate and crime rate are not significantly correlated with the UE in this study.
Choice of Units and the Causal Markov Condition
NASA Astrophysics Data System (ADS)
Zhang, Jiji; Spirtes, Peter
2014-03-01
Elliott Sober's well-known challenge to the principle of the common cause -- and to its generalization, the causal Markov condition -- appeals to the apparent positive correlation between two causally unconnected quantities: Venetian sea levels and British bread prices. In this paper we examine Kevin Hoover's and Daniel Steel's opposite evaluations of Sober's case. We argue that the difference in their assessments results from a difference in their choice of units and populations for statistical modeling. Our analysis suggests yet another diagnosis of Sober's counterexample: the failure of the causal Markov condition in the population chosen by Sober and Steel is due to the presence of causal relations that hold between the relevant properties across units. Such inter-unit causation is left unrepresented in causal models congenial to statistical analysis, because statistics does not deal with inter-unit relationships once the units are fixed. Accordingly, the causal Markov condition is formulated in terms of causal structures that depict intra-unit causal relations only. It is therefore worth highlighting a methodological principle for causal inference: the units should be so chosen that they do not interfere with each other, a principle that, fortunately, is often observed in practice.
Causal mediation analysis with a latent mediator.
Albert, Jeffrey M; Geng, Cuiyu; Nelson, Suchitra
2016-05-01
Health researchers are often interested in assessing the direct effect of a treatment or exposure on an outcome variable, as well as its indirect (or mediation) effect through an intermediate variable (or mediator). For an outcome following a nonlinear model, the mediation formula may be used to estimate causally interpretable mediation effects. This method, like others, assumes that the mediator is observed. However, as is common in structural equations modeling, we may wish to consider a latent (unobserved) mediator. We follow a potential outcomes framework and assume a generalized structural equations model (GSEM). We provide maximum-likelihood estimation of GSEM parameters using an approximate Monte Carlo EM algorithm, coupled with a mediation formula approach to estimate natural direct and indirect effects. The method relies on an untestable sequential ignorability assumption; we assess robustness to this assumption by adapting a recently proposed method for sensitivity analysis. Simulation studies show good properties of the proposed estimators in plausible scenarios. Our method is applied to a study of the effect of mother education on occurrence of adolescent dental caries, in which we examine possible mediation through latent oral health behavior. PMID:26363769
Shell Model in a First Principles Approach
Navratil, P; Nogga, A; Lloyd, R; Vary, J P; Ormand, W E; Barrett, B R
2004-01-08
We develop and apply an ab-initio approach to nuclear structure. Starting with the NN interaction, that fits two-body scattering and bound state data, and adding a theoretical NNN potential, we evaluate nuclear properties in a no-core approach. For presently feasible no-core model spaces, we evaluate an effective Hamiltonian in a cluster approach which is guaranteed to provide exact answers for sufficiently large model spaces and/or sufficiently large clusters. A number of recent applications are surveyed including an initial application to exotic multiquark systems.
Normalizing the causality between time series.
Liang, X San
2015-08-01
Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market. PMID:26382363
The causal meaning of Hamilton's rule.
Okasha, Samir; Martens, Johannes
2016-03-01
Hamilton's original derivation of his rule for the spread of an altruistic gene (rb>c) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton's rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton's rule is explored, by appeal to R. A. Fisher's 'average effect of a gene substitution'. PMID:27069669
Multisensory causal inference in the brain.
Kayser, Christoph; Shams, Ladan
2015-02-01
At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions. PMID:25710476
Causal Phenotype Discovery via Deep Networks
Kale, David C.; Che, Zhengping; Bahadori, Mohammad Taha; Li, Wenzhe; Liu, Yan; Wetzel, Randall
2015-01-01
The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the work in this area has focused on traditional statistical learning paradigms, such as classification, prediction, clustering, pattern mining. In this paper, we propose a related but different paradigm called causal phenotype discovery, which aims to discover latent representations of illness that are causally predictive. We illustrate this idea with a two-stage framework that combines the latent representation learning power of deep neural networks with state-of-the-art tools from causal inference. We apply this framework to two large ICU time series data sets and show that it can learn features that are predictively useful, that capture complex physiologic patterns associated with critical illnesses, and that are potentially more clinically meaningful than manually designed features. PMID:26958203
Normalizing the causality between time series
NASA Astrophysics Data System (ADS)
Liang, X. San
2015-08-01
Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market.
Lutz, V; Kjaer, J B; Iffland, H; Rodehutscord, M; Bessei, W; Bennewitz, J
2016-08-01
The objective of this research was to analyze the relationship between feather pecking (FP) and feather eating (FE) as well as general locomotor activity (GLA) using structural equation models, which allow that one trait can be treated as an explanatory variable of another trait. This provides an opportunity to infer putative causal links among the traits. For the analysis, 897 F2-hens set up from 2 lines divergently selected for high and low FP were available. The FP observations were Box-Cox transformed, and FE and GLA observations were log and square root transformed, respectively. The estimated heritabilities of FE, GLA, and FP were 0.36, 0.29, and 0.20, respectively. The genetic correlation between FP and FE (GLA) was 0.17 (0.04). A high genetic correlation of 0.47 was estimated between FE and GLA. The recursive effect from FE to FP was [Formula: see text], and from GLA to FP [Formula: see text] These results imply that an increase of FE leads to an increased FP behavior and that an increase in GLA results in a higher FP value. Furthermore, the study showed that the genetic correlation among the traits is mainly caused by indirect effects. PMID:27252366
Hishinuma, Earl S.; Chang, Janice Y.; McArdle, John J.; Hamagami, Fumiaki
2012-01-01
There is a relatively consistent negative relationship between adolescent depressive symptoms and educational achievement (e.g., grade-point average [GPA]). However, we are less certain of the causal direction for this association due to the lack of longitudinal data with both indicators measured across at least two time periods, and due to the lack of application of more sophisticated contemporary statistical techniques. We present multivariate results from a large longitudinal cohort-sequential study of high school students (N = 7,317) with measures of self-reported depressive symptoms and self-reported GPAs across multiple time points (following McArdle, 2009; McArdle et al., 2001) using an ethnically diverse sample from Hawai‘i. Contemporary statistical techniques included: bivariate dynamic structural equation modeling (DSEM); multi-group gender-and-ethnic DSEMs; ordinal scale measurement of key outcomes; and imputation for incomplete longitudinal data. The findings suggest that depressive symptoms affect subsequent academic achievement, and not the other way around, especially for Native Hawaiians as compared to non-Hawaiian females. We further discuss the scientific, applied, and methodological-statistical implications of the results, including the need for further theorizing and research on mediating variables. We also discuss the need for increased prevention, early intervention, screening, identification, and treatment of depressive symptoms and disorders. Finally, we argue for utilization of more contemporary methodological-statistical techniques, especially when violating parametric-test assumptions. PMID:22268606
2014-01-01
Background Group cognitive behavioural intervention (CBI) is effective in reducing low-back pain and disability in comparison to advice in primary care. The aim of this analysis was to investigate the impact of compliance on estimates of treatment effect and to identify factors associated with compliance. Methods In this multicentre trial, 701 adults with troublesome sub-acute or chronic low-back pain were recruited from 56 general practices. Participants were randomised to advice (control n = 233) or advice plus CBI (n = 468). Compliance was specified a priori as attending a minimum of three group sessions and the individual assessment. We estimated the complier average causal effect (CACE) of treatment. Results Comparison of the CACE estimate of the mean treatment difference to the intention-to-treat (ITT) estimate at 12 months showed a greater benefit of CBI amongst participants compliant with treatment on the Roland Morris Questionnaire (CACE: 1.6 points, 95% CI 0.51 to 2.74; ITT: 1.3 points, 95% CI 0.55 to 2.07), the Modified Von Korff disability score (CACE: 12.1 points, 95% CI 6.07 to 18.17; ITT: 8.6 points, 95% CI 4.58 to 12.64) and the Modified von Korff pain score (CACE: 10.4 points, 95% CI 4.64 to 16.10; ITT: 7.0 points, 95% CI 3.26 to 10.74). People who were non-compliant were younger and had higher pain scores at randomisation. Conclusions Treatment compliance is important in the effectiveness of group CBI. Younger people and those with more pain are at greater risk of non-compliance. Trial registration Current Controlled Trials ISRCTN54717854 PMID:24423146
Beaudoin, Christopher E; Chen, Hongliang; Agha, Sohail
2016-01-01
Rapid population growth in Pakistan poses major risks, including those pertinent to public health. In the context of family planning in Pakistan, the current study evaluates the Touch condom media campaign and its effects on condom-related awareness, attitudes, behavioral intention, and behavior. This evaluation relies on 3 waves of panel survey data from men married to women ages 15-49 living in urban and rural areas in Pakistan (N = 1,012): Wave 1 was March 15 to April 7, 2009; Wave 2 was August 10 to August 24, 2009; and Wave 3 was May 1 to June 13, 2010. Analysis of variance provided evidence of improvements in 10 of 11 condom-related outcomes from Wave 1 to Wave 2 and Wave 3. In addition, there was no evidence of outcome decay 1 year after the conclusion of campaign advertising dissemination. To help compensate for violating the assumption of random assignment, propensity score modeling offered evidence of the beneficial effects of confirmed Touch ad recall on each of the 11 outcomes in at least 1 of 3 time-lagged scenarios. By using these different time-lagged scenarios (i.e., from Wave 1 to Wave 2, from Wave 1 to Wave 3, and from Wave 2 to Wave 3), propensity score modeling permitted insights into how the campaign had time-variant effects on the different types of condom-related outcomes, including carryover effects of the media campaign. PMID:26855176
Application of transfer entropy to causality detection and synchronization experiments in tokamaks
NASA Astrophysics Data System (ADS)
Murari, A.; Peluso, E.; Gelfusa, M.; Garzotti, L.; Frigione, D.; Lungaroni, M.; Pisano, F.; Gaudio, P.; Contributors, JET
2016-02-01
Determination of causal-effect relationships can be a difficult task even in the analysis of time series. This is particularly true in the case of complex, nonlinear systems affected by significant levels of noise. Causality can be modelled as a flow of information between systems, allowing to better predict the behaviour of a phenomenon on the basis of the knowledge of the one causing it. Therefore, information theoretic tools, such as the transfer entropy, have been used in various disciplines to quantify the causal relationship between events. In this paper, Transfer Entropy is applied to determining the information relationship between various phenomena in Tokamaks. The proposed approach provides unique insight about information causality in difficult situations, such as the link between sawteeth and ELMs and ELM pacing experiments. The application to the determination of disruption causes, and therefore to the classification of disruption types, looks also very promising. The obtained results indicate that the proposed method can provide a quantitative and statistically sound criterion to address the causal-effect relationships in various difficult and ambiguous situations if the data is of sufficient quality.
Causal dynamical triangulation with extended interactions in 1+1 dimensions
NASA Astrophysics Data System (ADS)
Fuji, Hiroyuki; Sato, Yuki; Watabiki, Yoshiyuki
2011-10-01
We study the Causal Dynamical Triangulation (CDT) with extended interactions in 1 + 1 dimensions applying the method in the non-critical String Field Theory (SFT) constructed by Ishibashi and Kawai. For this model, we solve Schwinger-Dyson's Equation (SDE) for disk amplitude perturbatively, and find a matrix model in the continuum limit reproducing the SDE in the non-critical SFT approach as the loop equation.
MINIMIZING COGNITIVE ERRORS IN SITE-SPECIFIC CAUSAL ASSESSMENT
Interest in causal investigations in aquatic systems has been a natural outgrowth of the increased use of biological monitoring to characterize the condition of resources. Although biological monitoring approaches are critical tools for detecting whether effects are occurring, t...
Inductive Reasoning about Causally Transmitted Properties
ERIC Educational Resources Information Center
Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D.; Tenenbaum, Joshua B.
2008-01-01
Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates'…
Comments: Causal Interpretations of Mediation Effects
ERIC Educational Resources Information Center
Jo, Booil; Stuart, Elizabeth A.
2012-01-01
The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…
Heterogeneous Factor Analysis Models: A Bayesian Approach.
ERIC Educational Resources Information Center
Ansari, Asim; Jedidi, Kamel; Dube, Laurette
2002-01-01
Developed Markov Chain Monte Carlo procedures to perform Bayesian inference, model checking, and model comparison in heterogeneous factor analysis. Tested the approach with synthetic data and data from a consumption emotion study involving 54 consumers. Results show that traditional psychometric methods cannot fully capture the heterogeneity in…
Facet Modelling: An Approach to Flexible and Integrated Conceptual Modelling.
ERIC Educational Resources Information Center
Opdahl, Andreas L.; Sindre, Guttorm
1997-01-01
Identifies weaknesses of conceptual modelling languages for the problem domain of information systems (IS) development. Outlines an approach called facet modelling of real-world problem domains to deal with the complexity of contemporary analysis problems. Shows how facet models can be defined and visualized; discusses facet modelling in relation…
Contemporary Quantitative Methods and "Slow" Causal Inference: Response to Palinkas
ERIC Educational Resources Information Center
Stone, Susan
2014-01-01
This response considers together simultaneously occurring discussions about causal inference in social work and allied health and social science disciplines. It places emphasis on scholarship that integrates the potential outcomes model with directed acyclic graphing techniques to extract core steps in causal inference. Although this scholarship…
From Blickets to Synapses: Inferring Temporal Causal Networks by Observation
ERIC Educational Resources Information Center
Fernando, Chrisantha
2013-01-01
How do human infants learn the causal dependencies between events? Evidence suggests that this remarkable feat can be achieved by observation of only a handful of examples. Many computational models have been produced to explain how infants perform causal inference without explicit teaching about statistics or the scientific method. Here, we…
Causal Relations Drive Young Children's Induction, Naming, and Categorization
ERIC Educational Resources Information Center
Opfer, John E.; Bulloch, Megan J.
2007-01-01
A number of recent models and experiments have suggested that evidence of early category-based induction is an artifact of perceptual cues provided by experimenters. We tested these accounts against the prediction that different relations (causal versus non-causal) determine the types of perceptual similarity by which children generalize. Young…
Causal Coherence Relations and Levels of Discourse Representation
ERIC Educational Resources Information Center
Mulder, Gerben; Sanders, Ted J. M.
2012-01-01
This study focused on the cognitive representation of causal coherence relations linguistically marked with the connective "because." This article investigated whether these local causal relations are represented both at the level of the textbase and the situation model. Following earlier studies investigating the psychological validity of levels…
The Role of Functional Form in Causal-Based Categorization
ERIC Educational Resources Information Center
Rehder, Bob
2015-01-01
Two experiments tested how the "functional form" of the causal relations that link features of categories affects category-based inferences. Whereas "independent causes" can each bring about an effect by themselves, "conjunctive causes" all need to be present for an effect to occur. The causal model view of category…
Seeing Versus Doing: Two Modes of Accessing Causal Knowledge
ERIC Educational Resources Information Center
Waldmann, Michael R.; Hagmayer, York
2005-01-01
The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely…
ERIC Educational Resources Information Center
Hannan, Michael T.
This document is part of a series of chapters described in SO 011 759. Addressing the question of effective models to measure change and the change process, the author suggests that linear structural equation systems may be viewed as steady state outcomes of continuous-change models and have rich sociological grounding. Two interpretations of the…
Lombrozo, Tania
2010-12-01
Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. PMID:20801434
Stormwater infiltration trenches: a conceptual modelling approach.
Freni, Gabriele; Mannina, Giorgio; Viviani, Gaspare
2009-01-01
In recent years, limitations linked to traditional urban drainage schemes have been pointed out and new approaches are developing introducing more natural methods for retaining and/or disposing of stormwater. These mitigation measures are generally called Best Management Practices or Sustainable Urban Drainage System and they include practices such as infiltration and storage tanks in order to reduce the peak flow and retain part of the polluting components. The introduction of such practices in urban drainage systems entails an upgrade of existing modelling frameworks in order to evaluate their efficiency in mitigating the impact of urban drainage systems on receiving water bodies. While storage tank modelling approaches are quite well documented in literature, some gaps are still present about infiltration facilities mainly dependent on the complexity of the involved physical processes. In this study, a simplified conceptual modelling approach for the simulation of the infiltration trenches is presented. The model enables to assess the performance of infiltration trenches. The main goal is to develop a model that can be employed for the assessment of the mitigation efficiency of infiltration trenches in an integrated urban drainage context. Particular care was given to the simulation of infiltration structures considering the performance reduction due to clogging phenomena. The proposed model has been compared with other simplified modelling approaches and with a physically based model adopted as benchmark. The model performed better compared to other approaches considering both unclogged facilities and the effect of clogging. On the basis of a long-term simulation of six years of rain data, the performance and the effectiveness of an infiltration trench measure are assessed. The study confirmed the important role played by the clogging phenomenon on such infiltration structures. PMID:19587416
A multiscale modeling approach to adhesive contact
NASA Astrophysics Data System (ADS)
Fan, KangQi; Wang, WeiDong; Zhu, YingMin; Zhang, XiuYan
2011-09-01
In order to model the adhesive contact across different length scales, a multiscale approach is developed and used to study the adhesive contact behaviors between a rigid cylinder and an elastic face-centered cubic (FCC) substrate. The approach combines an atomistic treatment of the interfacial region with an elastic mechanics method description of the continuum region. The two regions are connected by a coupling region where nodes of the continuum region are refined to atoms of the atomistic region. Moreover, the elastic constants of FCC crystals are obtained directly from the Lennard-Jones potential to describe the elastic response characteristics of the continuum region, which ensures the consistency of material proprieties between atomistic and continuum regions. The multiscale approach is examined by comparing it with the pure MD simulation, and the results indicate that the multiscale modeling approach agrees well with the MD method in studying the adhesive contact behaviors.
Causal electromagnetic interaction equations
Zinoviev, Yury M.
2011-02-15
For the electromagnetic interaction of two particles the relativistic causal quantum mechanics equations are proposed. These equations are solved for the case when the second particle moves freely. The initial wave functions are supposed to be smooth and rapidly decreasing at the infinity. This condition is important for the convergence of the integrals similar to the integrals of quantum electrodynamics. We also consider the singular initial wave functions in the particular case when the second particle mass is equal to zero. The discrete energy spectrum of the first particle wave function is defined by the initial wave function of the free-moving second particle. Choosing the initial wave functions of the free-moving second particle it is possible to obtain a practically arbitrary discrete energy spectrum.
Causality & holographic entanglement entropy
NASA Astrophysics Data System (ADS)
Headrick, Matthew; Hubeny, Veronika E.; Lawrence, Albion; Rangamani, Mukund
2014-12-01
We identify conditions for the entanglement entropy as a function of spatial region to be compatible with causality in an arbitrary relativistic quantum field theory. We then prove that the covariant holographic entanglement entropy prescription (which relates entanglement entropy of a given spatial region on the boundary to the area of a certain extremal surface in the bulk) obeys these conditions, as long as the bulk obeys the null energy condition. While necessary for the validity of the prescription, this consistency requirement is quite nontrivial from the bulk standpoint, and therefore provides important additional evidence for the prescription. In the process, we introduce a codimension-zero bulk region, named the entanglement wedge, naturally associated with the given boundary spatial region. We propose that the entanglement wedge is the most natural bulk region corresponding to the boundary reduced density matrix.
Spatio-temporal Granger causality: a new framework
Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng
2015-01-01
That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924
Spatio-temporal Granger causality: a new framework.
Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng
2013-10-01
That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924
Spread of entanglement and causality
NASA Astrophysics Data System (ADS)
Casini, Horacio; Liu, Hong; Mezei, Márk
2016-07-01
We investigate causality constraints on the time evolution of entanglement entropy after a global quench in relativistic theories. We first provide a general proof that the so-called tsunami velocity is bounded by the speed of light. We then generalize the free particle streaming model of [1] to general dimensions and to an arbitrary entanglement pattern of the initial state. In more than two spacetime dimensions the spread of entanglement in these models is highly sensitive to the initial entanglement pattern, but we are able to prove an upper bound on the normalized rate of growth of entanglement entropy, and hence the tsunami velocity. The bound is smaller than what one gets for quenches in holographic theories, which highlights the importance of interactions in the spread of entanglement in many-body systems. We propose an interacting model which we believe provides an upper bound on the spread of entanglement for interacting relativistic theories. In two spacetime dimensions with multiple intervals, this model and its variations are able to reproduce intricate results exhibited by holographic theories for a significant part of the parameter space. For higher dimensions, the model bounds the tsunami velocity at the speed of light. Finally, we construct a geometric model for entanglement propagation based on a tensor network construction for global quenches.
Causality and the stability of parallel flow
NASA Astrophysics Data System (ADS)
Chimonas, George
1991-11-01
The linearized theory of the stability of parallel flow is formulated in terms of causality theory. In this method the properties of the Fourier spectrum—the disturbances with real frequency ω—provide the stability criteria for a system. In particular, if the Fourier spectrum provides a complete cause-and-effect formulation, the flow is assuredly stable. This single restriction leads to the critical-level theorems for parallel flows in both stably and unstably stratified fluids, Howard's semicircle theorem, the Miles-Howard Richardson-number criterion, an extended semicircle theorem for unstable flows, and several other results concerning instabilities in fluids. The causal approach supersedes group-velocity concepts, and resolves the problems of defining upward- and downward-propagating signals in a rapidly varying medium. It also associates a causal behavior to scattering from a discontinuity or an interface. Causality is examined by subdividing the continuous fluid into a set of homogeneous intervals within which propagation and interfacial scattering can be explicitly computed. As the intervals are reduced toward infinitesimal size the solution for the continuous fluid is obtained.
The Causal Effects of Father Absence.
McLanahan, Sara; Tach, Laura; Schneider, Daniel
2013-07-01
The literature on father absence is frequently criticized for its use of cross-sectional data and methods that fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models. Our assessment is that studies using more rigorous designs continue to find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children's social-emotional adjustment, and adult mental health. PMID:24489431
The Causal Effects of Father Absence
McLanahan, Sara; Tach, Laura; Schneider, Daniel
2014-01-01
The literature on father absence is frequently criticized for its use of cross-sectional data and methods that fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models. Our assessment is that studies using more rigorous designs continue to find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children’s social-emotional adjustment, and adult mental health. PMID:24489431
ERIC Educational Resources Information Center
Hannan, Michael T.
This document is part of a series of chapters described in SO 011 759. Stochastic models for the sociological analysis of change and the change process in quantitative variables are presented. The author lays groundwork for the statistical treatment of simple stochastic differential equations (SDEs) and discusses some of the continuities of…
Towards new approaches in phenological modelling
NASA Astrophysics Data System (ADS)
Chmielewski, Frank-M.; Götz, Klaus-P.; Rawel, Harshard M.; Homann, Thomas
2014-05-01
Modelling of phenological stages is based on temperature sums for many decades, describing both the chilling and the forcing requirement of woody plants until the beginning of leafing or flowering. Parts of this approach go back to Reaumur (1735), who originally proposed the concept of growing degree-days. Now, there is a growing body of opinion that asks for new methods in phenological modelling and more in-depth studies on dormancy release of woody plants. This requirement is easily understandable if we consider the wide application of phenological models, which can even affect the results of climate models. To this day, in phenological models still a number of parameters need to be optimised on observations, although some basic physiological knowledge of the chilling and forcing requirement of plants is already considered in these approaches (semi-mechanistic models). Limiting, for a fundamental improvement of these models, is the lack of knowledge about the course of dormancy in woody plants, which cannot be directly observed and which is also insufficiently described in the literature. Modern metabolomic methods provide a solution for this problem and allow both, the validation of currently used phenological models as well as the development of mechanistic approaches. In order to develop this kind of models, changes of metabolites (concentration, temporal course) must be set in relation to the variability of environmental (steering) parameters (weather, day length, etc.). This necessarily requires multi-year (3-5 yr.) and high-resolution (weekly probes between autumn and spring) data. The feasibility of this approach has already been tested in a 3-year pilot-study on sweet cherries. Our suggested methodology is not only limited to the flowering of fruit trees, it can be also applied to tree species of the natural vegetation, where even greater deficits in phenological modelling exist.
ERIC Educational Resources Information Center
Griffiths, Thomas L.; Tenenbaum, Joshua B.
2009-01-01
Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…
Causal Learning with Local Computations
ERIC Educational Resources Information Center
Fernbach, Philip M.; Sloman, Steven A.
2009-01-01
The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require…
The Development of Causal Categorization
ERIC Educational Resources Information Center
Hayes, Brett K.; Rehder, Bob
2012-01-01
Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs.…
Causal Inference and Developmental Psychology
ERIC Educational Resources Information Center
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
Cui, Long-Biao; Liu, Jian; Wang, Liu-Xian; Li, Chen; Xi, Yi-Bin; Guo, Fan; Wang, Hua-Ning; Zhang, Lin-Chuan; Liu, Wen-Ming; He, Hong; Tian, Ping; Yin, Hong; Lu, Hongbing
2015-01-01
Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions. PMID:26578933
Bastos-Leite, António J.; Ridgway, Gerard R.; Silveira, Celeste; Norton, Andreia; Reis, Salomé; Friston, Karl J.
2015-01-01
We report the first stochastic dynamic causal modeling (sDCM) study of effective connectivity within the default mode network (DMN) in schizophrenia. Thirty-three patients (9 women, mean age = 25.0 years, SD = 5) with a first episode of psychosis and diagnosis of schizophrenia—according to the Diagnostic and Statistic Manual of Mental Disorders, 4th edition, revised criteria—were studied. Fifteen healthy control subjects (4 women, mean age = 24.6 years, SD = 4) were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI) interspersed with 2 periods of continuous picture viewing. The anterior frontal (AF), posterior cingulate (PC), and the left and right parietal nodes of the DMN were localized in an unbiased fashion using data from 16 independent healthy volunteers (using an identical fMRI protocol). We used sDCM to estimate directed connections between and within nodes of the DMN, which were subsequently compared with t tests at the between subject level. The excitatory effect of the PC node on the AF node and the inhibitory self-connection of the AF node were significantly weaker in patients (mean values = 0.013 and −0.048 Hz, SD = 0.09 and 0.05, respectively) relative to healthy subjects (mean values = 0.084 and −0.088 Hz, SD = 0.15 and 0.77, respectively; P < .05). In summary, sDCM revealed reduced effective connectivity to the AF node of the DMN—reflecting a reduced postsynaptic efficacy of prefrontal afferents—in patients with first-episode schizophrenia. PMID:24939881
NASA Astrophysics Data System (ADS)
Runge, Jakob
2016-04-01
Causal reconstruction techniques from multivariate time series have become a popular approach to analyze interactions in complex systems such as the Earth. These approaches allow to exclude effects of common drivers and indirect influences. Practical applications are, however, especially challenging if nonlinear interactions are taken into account and for typically strongly autocorrelated climate time series. Here we discuss a new reconstruction approach with accompanying software package (Tigramite) and focus on two applications: (1) Information or perturbation transfer along causal pathways. This method allows to detect and quantify which intermediate nodes are important mediators of an interaction mechanism and is illustrated to disentangle pathways of atmospheric flow over Europe and for the ENSO - Indian Monsoon interaction mechanism. (2) A nonlinear model-free prediction technique that efficiently utilizes causal drivers and can be shown to yield information-theoretically optimal predictors avoiding over-fitting. The performance of this framework is illustrated on a climatological index of El Nino Southern Oscillation. References: Runge, J. (2015). Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 062829. doi:10.1103/PhysRevE.92.062829 Runge, J., Donner, R. V., & Kurths, J. (2015). Optimal model-free prediction from multivariate time series. Phys. Rev. E, 91(5), 052909. doi:10.1103/PhysRevE.91.052909 Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., … Kurths, J. (2015). Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. doi:10.1038/ncomms9502
On causality of extreme events
2016-01-01
Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available. PMID:27330866
CausalTrail: Testing hypothesis using causal Bayesian networks
Trampert, Patrick; Lenhof, Hans-Peter
2015-01-01
Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, hardly any program targeted at downstream analysis exists. In this paper we present CausalTrail, a tool for performing reasoning on causal Bayesian networks using the do-calculus. CausalTrail's features include multiple data import methods, a flexible query language for formulating hypotheses, as well as an intuitive graphical user interface. The program is able to account for missing data and thus can be readily applied in multi-omics settings where it is common that not all measurements are performed for all samples. Availability and Implementation CausalTrail is implemented in C++ using the Boost and Qt5 libraries. It can be obtained from https://github.com/dstoeckel/causaltrail PMID:26913195
Towards a Multiscale Approach to Cybersecurity Modeling
Hogan, Emilie A.; Hui, Peter SY; Choudhury, Sutanay; Halappanavar, Mahantesh; Oler, Kiri J.; Joslyn, Cliff A.
2013-11-12
We propose a multiscale approach to modeling cyber networks, with the goal of capturing a view of the network and overall situational awareness with respect to a few key properties--- connectivity, distance, and centrality--- for a system under an active attack. We focus on theoretical and algorithmic foundations of multiscale graphs, coming from an algorithmic perspective, with the goal of modeling cyber system defense as a specific use case scenario. We first define a notion of \\emph{multiscale} graphs, in contrast with their well-studied single-scale counterparts. We develop multiscale analogs of paths and distance metrics. As a simple, motivating example of a common metric, we present a multiscale analog of the all-pairs shortest-path problem, along with a multiscale analog of a well-known algorithm which solves it. From a cyber defense perspective, this metric might be used to model the distance from an attacker's position in the network to a sensitive machine. In addition, we investigate probabilistic models of connectivity. These models exploit the hierarchy to quantify the likelihood that sensitive targets might be reachable from compromised nodes. We believe that our novel multiscale approach to modeling cyber-physical systems will advance several aspects of cyber defense, specifically allowing for a more efficient and agile approach to defending these systems.
ERIC Educational Resources Information Center
Carroll, Glenn R.; And Others
This document is part of a series of chapters described in SO 011 759. The chapter examines the merits of four estimators in the causal analysis of event-histories (data giving the number, timing, and sequence of changes in a categorical dependent variable). The four procedures are ordinary least squares, Kaplan-Meier least squares, maximum…
A Conceptual Modeling Approach for OLAP Personalization
NASA Astrophysics Data System (ADS)
Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan
Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.
Post-16 Biology--Some Model Approaches?
ERIC Educational Resources Information Center
Lock, Roger
1997-01-01
Outlines alternative approaches to the teaching of difficult concepts in A-level biology which may help student learning by making abstract ideas more concrete and accessible. Examples include models, posters, and poems for illustrating meiosis, mitosis, genetic mutations, and protein synthesis. (DDR)
A causal examination of the effects of confounding factors on multimetric indices
Schoolmaster, Donald R., Jr.; Grace, James B.; Schweiger, E. William; Mitchell, Brian R.; Guntenspergen, Glenn R.
2013-01-01
The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.
Reconstructing Causal Biological Networks through Active Learning.
Cho, Hyunghoon; Berger, Bonnie; Peng, Jian
2016-01-01
Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments. PMID:26930205
Reconstructing Causal Biological Networks through Active Learning
Cho, Hyunghoon; Berger, Bonnie; Peng, Jian
2016-01-01
Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments. PMID:26930205
Building Water Models: A Different Approach
2015-01-01
Simplified classical water models are currently an indispensable component in practical atomistic simulations. Yet, despite several decades of intense research, these models are still far from perfect. Presented here is an alternative approach to constructing widely used point charge water models. In contrast to the conventional approach, we do not impose any geometry constraints on the model other than the symmetry. Instead, we optimize the distribution of point charges to best describe the “electrostatics” of the water molecule. The resulting “optimal” 3-charge, 4-point rigid water model (OPC) reproduces a comprehensive set of bulk properties significantly more accurately than commonly used rigid models: average error relative to experiment is 0.76%. Close agreement with experiment holds over a wide range of temperatures. The improvements in the proposed model extend beyond bulk properties: compared to common rigid models, predicted hydration free energies of small molecules using OPC are uniformly closer to experiment, with root-mean-square error <1 kcal/mol. PMID:25400877
Bell's theorem and the causal arrow of time
NASA Astrophysics Data System (ADS)
Argaman, Nathan
2010-10-01
Einstein held that the formalism of quantum mechanics involves "spooky actions at a distance." In the 1960s, Bell amplified this by showing that the predictions of quantum mechanics disagree with the results of any locally causal description. It should be appreciated that accepting nonlocal descriptions while retaining causality leads to a clash with relativity. Furthermore, the causal arrow of time by definition contradicts time-reversal symmetry. For these reasons, Wheeler and Feynman, Costa de Beauregard, Cramer, Price, and others have advocated abandoning microscopic causality. In this paper, a simplistic but concrete example of this line of thought is presented, in the form of a retro-causal toy model that is stochastic and provides an appealing description of the quantum correlations discussed by Bell. It is concluded that Einstein's "spooky actions" may occur "in the past" rather than "at a distance," resolving the tension between quantum mechanics and relativity and opening unexplored possibilities for future reformulations of quantum mechanics.
Causal sets and conservation laws in tests of Lorentz symmetry
Mattingly, David
2008-06-15
Many of the most important astrophysical tests of Lorentz symmetry also assume that energy momentum of the observed particles is exactly conserved. In the causal set approach to quantum gravity a particular kind of Lorentz symmetry holds but energy-momentum conservation may be violated. We show that incorrectly assuming exact conservation can give rise to a spurious signal of Lorentz symmetry violation for a causal set. However, the size of this spurious signal is much smaller than can be currently detected and hence astrophysical Lorentz symmetry tests as currently performed are safe from causal set induced violations of energy-momentum conservation.
History, causality, and sexology.
Money, John
2003-08-01
In 1896, Krafft-Ebing published Psychopathia Sexualis. Popularly defined as hereditary weakness or taintedness in the family pedigree, degeneracy was called upon as a causal explanation for perversions of the sexual instinct. Although Krafft-Ebing accepted Karl Ulrichs proposal that homosexuality could be innate and probably located in the brain, he paid little attention to neuropathological sexology. Alfred Binet challenged Krafft-Ebing's orthodoxy by explaining fetishism in terms of associative learning, to which Krafft-Ebing's response was that only those with a hereditary taint would be vulnerable. Thus did the venerable nature-nurture antithesis maintain its rhetoric, even to the present day. Krafft-Ebing died too soon to meet the Freudian challenge of endopsychic determinism, and too soon also to encounter the idea of a developmental multivariate outcome of what I have termed the lovemap. Like other brain maps, for example the languagemap, the lovemap requires an intact human brain in which to develop. The personalized content of the lovemap has access to the brain by way of the special senses. PMID:14533017
["Karoshi" and causal relationships].
Hamajima, N
1992-08-01
This paper aims to introduce a measure for use by physicians for stating the degree of probable causal relationship for "Karoshi", ie, a sudden death from cerebrovascular diseases or ischemic heart diseases under occupational stresses, as well as to give a brief description for legal procedures associated with worker's compensation and civil trial in Japan. It is a well-used measure in epidemiology, "attributable risk percent (AR%)", which can be applied to describe the extent of contribution to "Karoshi" of the excess occupational burdens the deceased worker was forced to bear. Although several standards such as average occupational burdens for the worker, average occupational burdens for an ordinary worker, burdens in a nonoccupational life, and a complete rest, might be considered for the AR% estimation, the average occupational burdens for an ordinary worker should normally be utilized as a standard for worker's compensation. The adoption of AR% could be helpful for courts to make a consistent judgement whether "Karoshi" cases are compensatable or not. PMID:1392028
Space, time, and causality in the human brain.
Woods, Adam J; Hamilton, Roy H; Kranjec, Alexander; Minhaus, Preet; Bikson, Marom; Yu, Jonathan; Chatterjee, Anjan
2014-05-15
The ability to perceive causality is a central human ability constructed from elemental spatial and temporal information present in the environment. Although the nature of causality has captivated philosophers and scientists since antiquity, the neural correlates of causality remain poorly understood. In the present study, we used functional magnetic resonance imaging (fMRI) to generate hypotheses for candidate brain regions related to component processes important for perceptual causality in the human brain: elemental space perception, elemental time perception, and decision-making (Experiment 1; n=16). We then used transcranial direct current stimulation (tDCS) to test neural hypotheses generated from the fMRI experiment (Experiment 2; n=16). In both experiments, participants judged causality in billiard-ball style launching events; a blue ball approaches and contacts a red ball. Spatial and temporal contributions to causal perception were assessed by parametrically varying the spatial linearity and the temporal delays of the movement of the balls. Experiment 1 demonstrated unique patterns of activation correlated with spatial, temporal, and decision-making components of causality perception. Using tDCS, we then tested hypotheses for the specific roles of the parietal and frontal cortices found in the fMRI experiment. Parietal stimulation only decreased participants' perception of causality based on spatial violations, while frontal stimulation made participants less likely to perceive causality based on violations of space and time. Converging results from fMRI and tDCS indicate that parietal cortices contribute to causal perception because of their specific role in processing spatial relations, while the frontal cortices contribute more generally, consistent with their role in decision-making. PMID:24561228
Detecting Causality In Space Plasmas With Entropy Based Measures
NASA Astrophysics Data System (ADS)
Johnson, J. R.; Wing, S.
2008-12-01
Understanding causal relationships in space plasmas is a key ingredient of modeling, but can often be difficult to establish. Frequently, causality is investigated by looking at the cross-correlation and checking for a shift in the peak as a function of lag time or by examining differences in the forward and backwards directions. Some of the shortcomings of this method can be illustrated by cross-correlations that exhibit multiple peaks that are large in the non-causal direction and causal systems that do not exhibit asymmetries in the cross-correlation. Furthermore, cross-correlations only reveal linear dependency and may not be as useful for a nonlinear storage and release dynamics (such as might be expected for the magnetospheric response to the solar wind). An alternative choice for studying causality is the one-sided transfer entropy which is highly directional and accounts for static internal correlations so that it is possible to examine whether two variables are driven by a common driver or whether they are causally connected. We apply the transfer entropy to several test systems to illustrate its utility for detecting causality and to space data to illustrate causality in space systems with examples from solar wind-magnetosphere coupling.
CAUSAL INFERENCE IN BIOLOGY NETWORKS WITH INTEGRATED BELIEF PROPAGATION
CHANG, RUI; KARR, JONATHAN R; SCHADT, ERIC E
2014-01-01
Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the ‘fitness’ of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot. PMID:25592596
A hybrid modeling approach for option pricing
NASA Astrophysics Data System (ADS)
Hajizadeh, Ehsan; Seifi, Abbas
2011-11-01
The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.
Causal inference and developmental psychology.
Foster, E Michael
2010-11-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference-tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of "ignorability." The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development. PMID:20677855
Principal stratification in causal inference.
Frangakis, Constantine E; Rubin, Donald B
2002-03-01
Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority. PMID:11890317
Causality assessment: A brief insight into practices in pharmaceutical industry.
Naidu, R Purushotham
2013-10-01
Healthcare industry is flooded with multitude of drugs, and the list is increasing day by day. Consumption of medications has enormously increased due to life style changes, having safer drugs is the need of the hour. Regulators and other authorities to have a check have put in stringent regulations and pharmacovigilance system in place. Eventhough there has been increase in adverse drug reactions (ADR) reporting in the last decade, causality assessment has been the greater challenge for academicians and even industry. Causality is crucial for risk benefit assessment, particularly when it involves post marketing safety signals. Pharmaceutical companies have put in efforts to have a standardized approach for causality assessment. This article will provide some insight into the approaches for causality assessment from a pharma industry perspective. PMID:24312892
Nonperturbative approach to the attractive Hubbard model
Allen, S.; Tremblay, A.-M. S.
2001-08-15
A nonperturbative approach to the single-band attractive Hubbard model is presented in the general context of functional-derivative approaches to many-body theories. As in previous work on the repulsive model, the first step is based on a local-field-type ansatz, on enforcement of the Pauli principle and a number of crucial sumrules. The Mermin-Wagner theorem in two dimensions is automatically satisfied. At this level, two-particle self-consistency has been achieved. In the second step of the approximation, an improved expression for the self-energy is obtained by using the results of the first step in an exact expression for the self-energy, where the high- and low-frequency behaviors appear separately. The result is a cooperon-like formula. The required vertex corrections are included in this self-energy expression, as required by the absence of a Migdal theorem for this problem. Other approaches to the attractive Hubbard model are critically compared. Physical consequences of the present approach and agreement with Monte Carlo simulations are demonstrated in the accompanying paper (following this one).
A subgrid based approach for morphodynamic modelling
NASA Astrophysics Data System (ADS)
Volp, N. D.; van Prooijen, B. C.; Pietrzak, J. D.; Stelling, G. S.
2016-07-01
To improve the accuracy and the efficiency of morphodynamic simulations, we present a subgrid based approach for a morphodynamic model. This approach is well suited for areas characterized by sub-critical flow, like in estuaries, coastal areas and in low land rivers. This new method uses a different grid resolution to compute the hydrodynamics and the morphodynamics. The hydrodynamic computations are carried out with a subgrid based, two-dimensional, depth-averaged model. This model uses a coarse computational grid in combination with a subgrid. The subgrid contains high resolution bathymetry and roughness information to compute volumes, friction and advection. The morphodynamic computations are carried out entirely on a high resolution grid, the bed grid. It is key to find a link between the information defined on the different grids in order to guaranty the feedback between the hydrodynamics and the morphodynamics. This link is made by using a new physics-based interpolation method. The method interpolates water levels and velocities from the coarse grid to the high resolution bed grid. The morphodynamic solution improves significantly when using the subgrid based method compared to a full coarse grid approach. The Exner equation is discretised with an upwind method based on the direction of the bed celerity. This ensures a stable solution for the Exner equation. By means of three examples, it is shown that the subgrid based approach offers a significant improvement at a minimal computational cost.
Spontaneous Assimilation of Continuous Values and Temporal Information in Causal Induction
ERIC Educational Resources Information Center
Marsh, Jessecae K.; Ahn, Woo-kyoung
2009-01-01
Existing models of causal induction primarily rely on the contingency between the presence and the absence of a causal candidate and an effect. Yet, classification of observations into these four types of covariation data may not be straightforward because (a) most causal candidates, in real life, are continuous with ambiguous, intermediate values…
A Bayesian Shrinkage Approach for AMMI Models.
da Silva, Carlos Pereira; de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves; Balestre, Marcio
2015-01-01
Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior
A Bayesian Shrinkage Approach for AMMI Models
de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves
2015-01-01
Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior