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IDENTIFYING MECHANISMS BEHIND POLICY INTERVENTIONS VIA CAUSAL MEDIATION ANALYSIS
Author(s) -
Keele Luke,
Tingley Dustin,
Yamamoto Teppei
Publication year - 2015
Publication title -
journal of policy analysis and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.898
H-Index - 84
eISSN - 1520-6688
pISSN - 0276-8739
DOI - 10.1002/pam.21853
Subject(s) - mediation , mechanism (biology) , identification (biology) , causal analysis , outcome (game theory) , causal inference , causal model , causal structure , psychological intervention , causal chain , computer science , intervention (counseling) , management science , psychology , risk analysis (engineering) , econometrics , sociology , economics , medicine , epistemology , microeconomics , social science , philosophy , botany , physics , pathology , quantum mechanics , psychiatry , biology
Causal analysis in program evaluation has primarily focused on the question about whether or not a program, or package of policies, has an impact on the targeted outcome of interest. However, it is often of scientific and practical importance to also explain why such impacts occur. 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 discuss an extension of 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 pre‐school classes and job‐training workshops.