Department of Statistics, UCLA
The Science and Ethics of Causal Modeling
- Author(s): Pearl, Judea
- et al.
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.