Nonparametric efficient inference for network quantile causal effects under partial interference

TL;DR

This paper develops a nonparametric, efficient method for estimating network causal effects on outcome quantiles under partial interference, with robust theoretical guarantees and practical performance.

Contribution

It introduces a novel nonparametric efficiency theory and estimator for network quantile causal effects in clustered settings with interference.

Findings

Estimator is consistent and asymptotically normal with parametric rates.

Simulation studies show good finite-sample performance.

Applied method to a real clustered observational dataset.

Abstract

Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interference occurs only among individuals within the same cluster, a setting referred to as partial interference. We study network causal effects on outcome quantiles in the presence of partial interference. We develop a general nonparametric efficiency theory for estimating these network quantile causal effects, which leads to a nonparametrically efficient estimator. The proposed estimator is consistent and asymptotically normal with parametric convergence rates, while allowing for flexible, data-adaptive estimation of complex nuisance functions. We leverage a three-way cross-fitting procedure that avoids direct estimation of the conditional outcome distribution. Simulations demonstrate adequate finite-sample performance of the proposed estimators, and we apply the methods to a clustered observational study.