Statistical Analysis of Causal Mechanisms: Identification, Inference, and Sensitivity Analysis

Princeton University, Princeton NJ

Investigators

Abstract

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Causal inference is the central goal of most social science research. In recent years, empirical researchers are increasingly relying on experiments in order to improve the validity of causal conclusions. At the same time, the methodological literature on causal inference has flourished. Despite these two developments, experiments confront a fundamental weakness since they only provide a black-box view of causality. The randomization of treatment makes it possible for researchers to obtain a valid estimate of treatment effect and thereby determine whether the treatment causally affects the outcome. However, randomized experiments do not provide much information about the underlying causal mechanisms that have brought about such causal effects. They do not answer the important questions of how and why the treatment causally affects the outcome. This is an important limitation because the identification of causal mechanisms is often required to test the validity of competing theories that offer different explanations about causal effects. The goal of the proposed research is to overcome this limitation by developing a set of new statistical methods for the analysis of causal mechanisms. Specifically, the investigator focuses on causal mediation analysis where causal mechanisms are examined by estimating direct and indirect effects. In this framework, alternative causal explanations are investigated by examining the roles of intermediate variables that lie in the causal path between the treatment and outcome variables. The proposed research addresses three important aspects of causal mediation analysis. First, identification analysis establishes a minimum set of assumptions that are required to ascertain direct and indirect effects and clarifies the exact degree to which the data are informative about particular causal mechanisms. Second, the proposed development of parametric and nonparametric estimators will make inference from the observed data possible and allow researchers to compute uncertainty estimates as well as point estimates. Third, sensitivity analysis is essential for causal mediation analysis which requires a strong identifying assumption. The proposed methods for sensitivity analysis will allow applied researchers to examine the robustness of their empirical findings to the possible violation of the key identifying assumption.