Abstract not found

Abstract: This review presents empirical researchers with 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 potential-outcome frameworks and presents tools for a symbiosis analysis that uses the strong features of both.

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.

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 1950-60s, 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 potential-outcome frameworks, and develops symbiotic tools that use the strong features of both.

The "potential outcome," or Neyman-Rubin (NR) model through which statisticians were first introduced to causal analysis suffers from two fundamental shortcomings: (1) It lacks formal underpinning and (2) it uses conceptually opaque language for expressing causal information. As a results, investigators find it difficult to discern whether a set of formulae represents a faithful picture of one's knowledge, and whether such a set is self-consistent or redundant. These shortcomings can be rectified using counterfactual semantics based on nonparametric structural equations [Pearl, 2000a] which provides both a mathematical foundation for the NR analysis and a conceptually transparent language for expressing causal knowledge. This semantical framework gives rise to a friendly calculus of causation that unifies the graphical, potential outcome and structural equation approaches and resolves longstanding problems in several of the sciences. These include questions of confounding, causal effect estimation, policy analysis, legal responsibility, direct and indirect effects, instrumental variables, surrogate designs, and the integration of data from experimental and observational studies.

Abstract: 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 potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both.

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 (Neyman-Rubin) and structural equation approaches and resolves long-standing 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.

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 potential-outcome frameworks and demonstrates a symbiotic analysis that uses the strong features of both.

helsinki.fi Evaluating methods for computer-assisted stemmatology using artificial benchmark data sets

Multi-agent social-network models are becoming increasingly used due to their power and flexibility in capturing emergent behaviors in complex socio-technical 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 knowledge-based simulation-aided approach for automating model-improvement and our tool to implement this approach (WIZER). WIZER is capable of calibrating and validating multi-agent social-network models, and facilitates model-improvement and understanding. By employing knowledge-based 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 city-scale multi-agent social-network model capable of simulating the effects of weaponized biological attacks on a demographically-realistic population against a background of naturally-occurring diseases. The results show the efficacy of WIZER.