Analysis of Randomized Experiments with Network Interference and Noncompliance

TL;DR

This paper develops a framework for analyzing randomized experiments with network interference and noncompliance, incorporating strategic treatment choices and unobserved heterogeneity, and proposes a control function estimator with asymptotic properties.

Contribution

It introduces a novel approach combining game-theoretic treatment choice modeling with random coefficient outcomes, addressing noncompliance and spillovers in network experiments.

Findings

Evidence of spillover effects on bed net adoption.

Method applied to subsidy policy analysis.

Estimator demonstrated to have desirable asymptotic properties.

Abstract

Randomized experiments have become a standard tool in economics. In analyzing randomized experiments, the traditional approach has been based on the Stable Unit Treatment Value (SUTVA: \cite{rubin}) assumption which dictates that there is no interference between individuals. However, the SUTVA assumption fails to hold in many applications due to social interaction, general equilibrium, and/or externality effects. While much progress has been made in relaxing the SUTVA assumption, most of this literature has only considered a setting with perfect compliance to treatment assignment. In practice, however, noncompliance occurs frequently where the actual treatment receipt is different from the assignment to the treatment. In this paper, we study causal effects in randomized experiments with network interference and noncompliance. Spillovers are allowed to occur at both treatment choice stage and outcome realization stage. In particular, we explicitly model treatment choices of agents as a binary game of incomplete information where resulting equilibrium treatment choice probabilities affect outcomes of interest. Outcomes are further characterized by a random coefficient model to allow for general unobserved heterogeneity in the causal effects. After defining our causal parameters of interest, we propose a simple control function estimator and derive its asymptotic properties under large-network asymptotics. We apply our methods to the randomized subsidy program of \cite{dupas} where we find evidence of spillover effects on both short-run and long-run adoption of insecticide-treated bed nets. Finally, we illustrate the usefulness of our methods by analyzing the impact of counterfactual subsidy policies.