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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.

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and their relation to thermodynamics

We provide a formal definition of the notion of “transportability, ” or “external validity, ” which we view as a license to transfer causal information learned in experimental studies to a different environment, in which only observational studies can be conducted. We introduce a formal representation called “selection diagrams ” for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we derive procedures for deciding whether causal effects in the target environment can be inferred from experimental findings in a different environment. When the answer is affirmative, the procedures identify the set of experimental and observational studies that need be conducted to license the transport. We further demonstrate how transportability analysis can guide the transfer of knowledge among non-experimental studies to minimize re-measurement cost and improve prediction power. We further provide a causally principled definition of “surrogate endpoint ” and show that the theory of transportability can assist the identification of valid surrogates in a complex network of cause-effect relationships. 1 Introduction: Threats

Consider the query: Does a binary treatment X have a causal effect on a response Y through a causal pathway that does not involve the intermediate variable M? This query is often rephrased as: Does X have a direct causal effect on Y not through M? Direct effects have been formally defined in three different ways: the controlled direct effects (CDE), the natural direct effects (i.e. pure and total direct effects- PDE and TDE), and the principal stratum direct effects (PSDE). In this issue of the journal, Hafeman and VanderWeele (H&V) 7 provide novel minimal or near minimal conditions for identification of the CDE, PDE and TDE but do not consider the PSDE. In this commentary, we review inference for direct effects and the results of H&V. We also review the close relationship between the direct effects literature and the literature on instrumental variables and Mendelian randomization. 1 Formal Definitions To proceed, we review the formal definitions of the three types of direct effects. We first consider a study with baseline covariates C, a dichotomous treatment X

We consider four classes of graphical causal models: the Finest Fully Randomized Causally

This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on non-parametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 1950-60s, 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 the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and non-linear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and

Principal stratification has recently become a popular tool to address certain causal inference questions particularly in dealing with post-randomization factors in randomized trials. Here we analyze the conceptual basis for this framework and invite response to clarify the value of principal stratification in estimating causal effects of interest.

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