Department of Statistics, UCLA

The intrinsic schism between causal and associational relations presents profound ethical and methodological challenges 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 challenges by offering a simple, yet formal and principled methodology for causal analysis in empirical research. The framework is based on the Structural Causal Model (SCM) described in (Pearl, 2000) -- a non-parametric 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.