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Activity Number: 391 - Causal Inference Under Interference
Type: Invited
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #316607
Title: Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments
Author(s): Zhichao Jiang* and Kosuke Imai
Companies: University of Massachusetts Amherst and Harvard University
Keywords: experimental design; interference between units; partial interference; spillover effects; statistical power

Two-stage randomized experiments are becoming an increasingly popular experimental design for causal inference when the outcome of one unit may be affected by the treatment assignments of other units in the same cluster. In this paper, we provide a methodological framework for developing general tools of statistical inference and power analysis for two-stage randomized experiments. Under the randomization-based framework, we propose unbiased point estimators of direct and spillover effects, construct conservative variance estimators, develop hypothesis testing procedures, and derive sample size formulas. We also establish the equivalence relationship between the randomization-based and regression-based methods. Our methodology can be easily adapted for the cluster and individual randomized experiments, which represent two limiting designs of two-stage randomized experiments. Finally, we conduct simulation studies to evaluate the empirical performance of our sample size formulas. For empirical illustration, the proposed methodology is applied to the analysis of the data from a field experiment on a job placement assistance program.

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

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