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Primary Submission Category: Causal Inference and SUTVA/Consistencies Violations

Efficient Weighted Estimators of Interference Effects under Hypothetical Treatment Allocations in Two-Stage Randomized Experiments under Bernoulli Assignment

Authors: Colleen Chan, Shinpei Nakamura Sakai, Laura Forastiere,

Presenting Author: Colleen Chan*

In many applications, the no-interference assumption in causal inference is often violated as individuals often interact with one another. Two-stage randomized experiments are incredibly useful designs for estimating causal effects of a given treatment in the presence of interference. In this design, clusters are assigned a treatment saturation level in the first stage, and each unit within a cluster is randomized to treatment or control according to the assigned saturation level in the second stage. Previous two-stage designs have been proposed under complete randomization in both stages, and simple difference-in-means estimators have been developed under the partial interference assumption. However, complete randomization in the second stage only allows the estimation of causal effects under the treatment saturations of the first stage. We propose instead a Bernoulli assignment in the second stage and weighted estimators of direct and spillover effects, combining information from all clusters. One clear advantage of using Bernoulli assignment is that it allows researchers to estimate causal effects under hypothetical treatment allocations. We derive cluster weights achieving the optimal bias-variance trade-off for our estimator. We develop simulation studies to analyze the finite sample performance of our proposed estimators. Finally, we illustrate our methodology with a data-inspired information campaign to prevent anemia in India.