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.

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