Identifying Mechanisms behind Policy Interventions via Causal Mediation Analysis

Causal analysis in program evaluation has largely focused on the assessment of policy effectiveness. That is, researchers typically study whether a program has a causal effect on the outcome of interest. However, it is often of scientific and practical importance to also explain why such a causal relationship arises. In this paper, we introduce causal mediation analysis, a statistical framework for analyzing causal mechanisms that has become increasingly popular in social and medical sciences in recent years. The framework enables us to show exactly what assumptions are sufficient for identifying causal mediation effects for the mechanisms of interest, derive a general algorithm for estimating such mechanism-specific effects, and formulate a sensitivity analysis for the violation of those identification assumptions. We also extend the framework to analyze causal mechanisms in the presence of treatment noncompliance, a common problem in randomized evaluation studies. The methods are illustrated via applications to two intervention studies on school class sizes and job training workshops.