Biostatistics Seminar Series: Kosuke Imai, PhD

Title: Causal Inference with Interference and Noncompliance in the Two-Stage Randomized Experiments Abstract: In many social science experiments, subjects often interact with each other and as a result one's treatment often influences the outcome of another unit.  Over the last decade, a significant progress has been made towards causal inference in the presence of such interference among units.  In particular, researchers have shown that the two-stage randomization of treatment assignment enables the identification of average spillover and direct effects.  However, much of the literature has assumed perfect compliance with treatment assignment. In this paper, we derive the nonparametric identification conditions for the average complier direct effect (ACDE) in two-stage randomized experiments with interference and noncompliance.  In particular, we consider the spillover effect of the encouragement on the treatment as well as the spillover effect of the treatment on the outcome.  We then propose a consistent Neyman-type estimator of the ACDE and derive its variance under the stratified interference assumption.  Finally, we establish the exact relationship between the proposed Neyman-type estimator and the two-stage least squares estimator.  (Joint work with Zhichao Jiang)