Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference.

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 e

SUBMITTER: Lee C 

PROVIDER: S-EPMC12096343 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature