Teppei Yamamoto is an Assistant Professor of Political Science at Massachusetts Institute of Technology. Professor Yamamoto is broadly interested in the development of quantitative methods for political science data. His research has focused on statistical methods for causal inference, including causal attribution, causal mediation, causal moderation, and causal inference with measurement error. Professor Yamamoto also studies applied Bayesian statistics, with a focus on discrete choice models and empirical applications in electoral studies and comparative political behavior.
Professor Yamamoto's talk is entitled: Identification and Estimation of Causal Mediation Effects with Treatment Noncompliance.
Treatment noncompliance is a common problem in program evaluation. The problem is particularly severe when the analyst is interested in causal mediation effects. This is because, somewhat counterintuitively, the mediated portion of an intention-to-treat (ITT) effect cannot be nonparametrically identified even when treatment assignment is randomized and the ignorability of the observed mediator is assumed. This paper shows that, once the standard instrumental variables assumptions are made, the mediated ITT effects and the local average causal mediation effects (LACME) for compliers can in fact be identified under a local sequential ignorability assumption. The commonly-used naïve estimators for the mediated ITT effect and LACME are shown to be biased in unknown directions. The proposed estimators are illustrated via a simulation study and applied to data from a job training experiment. The proposed method, implemented in an open-source R package, enables researchers to investigate causal mechanisms by which the treatment affects the outcome of interest even when treatment noncompliance exists.