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Causal inference in statistics: An Overview
, 2009
"... This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all ca ..."
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Cited by 37 (9 self)
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This review presents empirical researcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects ” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret, ” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potentialoutcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.
Estimating highdimensional intervention effects from observation data
 THE ANN OF STAT
, 2009
"... We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can ..."
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Cited by 9 (2 self)
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We assume that we have observational data generated from an unknown underlying directed acyclic graph (DAG) model. A DAG is typically not identifiable from observational data, but it is possible to consistently estimate the equivalence class of a DAG. Moreover, for any given DAG, causal effects can be estimated using intervention calculus. In this paper, we combine these two parts. For each DAG in the estimated equivalence class, we use intervention calculus to estimate the causal effects of the covariates on the response. This yields a collection of estimated causal effects for each covariate. We show that the distinct values in this set can be consistently estimated by an algorithm that uses only local information of the graph. This local approach is computationally fast and feasible in highdimensional problems. We propose to use summary measures of the set of possible causal effects to determine variable importance. In particular, we use the minimum absolute value of this set, since that is a lower bound on the size of the causal effect. We demonstrate the merits of our methods in a simulation study and on a data set about riboflavin production.
Integrating experimental and observational personality research – the contributions of Hans Eysenck
 Journal of Personality
, 2008
"... A fundamental aspect of Hans Eysenck’s research was his emphasis upon using all the tools available to the researcher to study personality. This included correlational, experimental, physiological, and genetic approaches. 50 years after Cronbach’s call for the reunification of the two disciplines of ..."
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Cited by 6 (6 self)
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A fundamental aspect of Hans Eysenck’s research was his emphasis upon using all the tools available to the researcher to study personality. This included correlational, experimental, physiological, and genetic approaches. 50 years after Cronbach’s call for the reunification of the two disciplines of psychology (Cronbach, 1957) and 40 years after Eysenck’s plea for experimental approaches to personality research (H. J. Eysenck, 1966), what is the status of the unification? Should personality researchers use experimental techniques? Do experimental techniques allow us to tease out causality, and are we communicating the advantages of combining experimental with multivariate correlational techniques? We review the progress made since Cronbach and Eysenck’s original papers and suggest that although it is still uncommon to find experimental studies of personality, psychology would benefit from the joint use of correlational and experimental approaches.
Causality in the Social and Behavioral Sciences
 A PAPER SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2009
"... This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illust ..."
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Cited by 1 (1 self)
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This paper aims to acquaint researchers in the quantitative social and behavior sciences with recent advances in causal inference which provide a systematic methodology for defining, estimating, testing, and defending causal claims in experimental and observational studies. These advances are illustrated using a general theory of causation based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, which provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”) (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attribution” or “causes of effects”) and (3) queries about direct and indirect effects (also known as “mediation”). Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and potentialoutcome frameworks, and develops symbiotic tools that use the strong features of both.
U: Modeling of environmental effects in genomewide association studies identifies SLC2A2 and HP as novel loci influencing serum cholesterol levels. PLoS Genet 2010
 Rudan P, Gnewuch C, Schmitz G, Meitinger T, Pramstaller PP, Hicks AA, Oostra
"... Genomewide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholester ..."
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Genomewide association studies (GWAS) have identified 38 larger genetic regions affecting classical blood lipid levels without adjusting for important environmental influences. We modeled diet and physical activity in a GWAS in order to identify novel loci affecting total cholesterol, LDL cholesterol, HDL cholesterol, and triglyceride levels. The Swedish (SE) EUROSPAN cohort (N SE = 656) was screened for candidate genes and the nonSwedish (NS) EUROSPAN cohorts (N NS = 3,282) were used for replication. In total, 3 SNPs were associated in the Swedish sample and were replicated in the nonSwedish
The Mathematics of Causal Relations
, 2008
"... This paper introduces empirical researchers to recent advances in causal inference and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all caus ..."
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Cited by 1 (0 self)
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This paper introduces empirical researchers to recent advances in causal inference and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. In particular, the paper advocates a formalism based on nonparametric structural equations [Pearl, 2000a] which provides both a mathematical foundation for the analysis of counterfactuals and a conceptually transparent language for expressing causal knowledge. This framework gives rise to a friendly calculus of causation that uni es the graphical, potential outcome (NeymanRubin) and structural equation approaches and resolves longstanding problems in several of the sciences. These include questions of confounding, causal e ect estimation, policy analysis, legal responsibility, direct and indirect e ects, instrumental variables, surrogate designs, and the integration of data from experimental and observational studies.
WIZER: An Automated Intelligent Tool for Model Improvement of MultiAgent SocialNetwork Systems
"... Multiagent socialnetwork models are becoming increasingly used due to their power and flexibility in capturing emergent behaviors in complex sociotechnical systems and their ability to link to real data. These models are growing in size and complexity which requires significant time and effort to ..."
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Multiagent socialnetwork models are becoming increasingly used due to their power and flexibility in capturing emergent behaviors in complex sociotechnical systems and their ability to link to real data. These models are growing in size and complexity which requires significant time and effort to calibrate, validate, improve the model, and gain insight into model behavior. In this paper, we present our knowledgebased simulationaided approach for automating modelimprovement and our tool to implement this approach (WIZER). WIZER is capable of calibrating and validating multiagent socialnetwork models, and facilitates modelimprovement and understanding. By employing knowledgebased search, causal analysis, and simulation control and inference techniques, WIZER can reduce the number of simulation runs needed to calibrate, validate, and improve a model and improve the focus of these runs. We ran WIZER on BioWar – a cityscale multiagent socialnetwork model capable of simulating the effects of weaponized biological attacks on a demographicallyrealistic population against a background of naturallyoccurring diseases. The results show the efficacy of WIZER.
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"... helsinki.fi Evaluating methods for computerassisted stemmatology using artificial benchmark data sets ..."
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helsinki.fi Evaluating methods for computerassisted stemmatology using artificial benchmark data sets
The Science and Ethics of Causal Modeling
, 2010
"... The intrinsic schism between causal and associational relations presents profound ethical and methodological problems to researchers in the social and behavioral sciences, ranging from the statement of a problem, to the implementation of a study, to the reporting of finding. This paper describes a c ..."
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The intrinsic schism between causal and associational relations presents profound ethical and methodological problems to researchers in the social and behavioral sciences, ranging from the statement of a problem, to the implementation of a study, to the reporting of finding. This paper describes a causal modeling framework that mitigates these problems and offers a simple, yet formal and principled methodology for empirical research. The framework is based on the Structural Causal Model (SCM) described in [Pearl, 2000b] – a nonparametric extension of structural equation models that provides a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper establishes a methodology for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”), (2) queries about probabilities of counterfactuals, (including assessment of “regret,” “attribution,” or “causes of effects”), and (3) queries about direct and indirect effects (also known as “mediation” or “effect decomposition”). Finally, the paper defines the formal and conceptual relationships between the structural and potentialoutcome frameworks and demonstrates a symbiotic analysis that uses the strong features of both.