Nonparametric Causal Survival Analysis with Clustered Interference

TL;DR

This paper introduces nonparametric methods for causal survival analysis in clustered interference settings, addressing challenges like censoring and confounding, and enabling flexible inference for various intervention policies.

Contribution

It develops a novel nonparametric approach that handles confounding, censoring, and clustered interference without parametric assumptions, allowing for flexible policy evaluation.

Findings

Estimators are consistent and asymptotically normal.

Simulation studies show good finite sample performance.

Applied to cholera vaccine data in Bangladesh.

Abstract

Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of other units, i.e., there is interference. In some settings, units may be grouped into clusters such that it is reasonable to assume interference only occurs within clusters, i.e., there is clustered interference. In this paper, methods are developed which can accommodate confounding, censored outcomes, and clustered interference. The approach avoids parametric assumptions and permits inference about counterfactual scenarios corresponding to any stochastic policy which modifies the propensity score distribution, and thus may have application across diverse settings. The proposed nonparametric sample splitting estimators allow for flexible data-adaptive estimation of nuisance functions and are consistent and asymptotically normal with parametric convergence rates. Simulation studies demonstrate the finite sample performance of the proposed estimators, and the methods are applied to a cholera vaccine study in Bangladesh.