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Activity Number: 650 - Relaxing No Interference Assumptions in Clustered Randomized Trials
Type: Invited
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract #326758
Title: Causal Inference with Interference and Noncompliance in the Two-Stage Randomized Experiments
Author(s): Zhichao Jiang* and Kosuke Imai and Anup Malani
Companies: Princeton University and Princeton University and University of Chicago
Keywords: complier average causal effects; Interference; Noncompliance; Randomized experiments; Two-stage least squares

In many social science experiments, subjects often interact with each other and as a result one's treatment influences the outcome of another unit. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. In this paper, we establish the nonparametric identification of the complier average direct and spillover effects in two-stage randomized experiments with interference and noncompliance. In particular, we consider the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. We propose consistent estimators and derive their variances under the stratified interference assumption. We prove the exact relationship between the proposed randomization-based estimators and the popular two-stage least squares estimators. Our methodology is motivated by and applied to the randomized evaluation of the Indian health insurance program, where we find some evidence of spillover effects on both treatment take-up and outcome.

Authors who are presenting talks have a * after their name.

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