Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference

TL;DR

This paper introduces nonparametric estimators for causal effects under clustered interference, allowing flexible, data-adaptive analysis of treatment impacts within clusters, demonstrated through simulations and a real-world application in Senegal.

Contribution

It proposes new nonparametric causal estimands based on propensity score modifications that are more relevant and do not rely on parametric models, along with efficient estimators.

Findings

Estimators are consistent, asymptotically normal, and efficient.

Simulation results show good finite sample performance.

Application to Senegal data illustrates practical utility.

Abstract

Interference occurs when a unit's treatment (or exposure) affects another unit's outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference, if present, only occurs between individuals in the same cluster, i.e., there is clustered interference. Various causal estimands have been proposed to quantify treatment effects under clustered interference from observational data, but these estimands either entail treatment policies lacking real-world relevance or are based on parametric propensity score models. Here, we propose new causal estimands based on modification of the propensity score distribution which may be more relevant in many contexts and are not based on parametric models. Nonparametric sample splitting estimators of the new estimands are constructed, which allow for flexible data-adaptive estimation of nuisance functions and are consistent, asymptotically normal, and efficient, converging at the usual parametric rate. Simulations show the finite sample performance of the proposed estimators. The proposed methods are applied to evaluate the effect of water, sanitation, and hygiene facilities on diarrhea among children in Senegal.