Nonparametric efficient inference for network quantile causal effects under partial interference
TLDR
This paper introduces a nonparametric efficient estimator for network quantile causal effects under partial interference using a novel cross-fitting procedure.
Key contributions
- Develops a general nonparametric efficiency theory for network quantile causal effects.
- Proposes a nonparametrically efficient estimator for these effects under partial interference.
- Utilizes a three-way cross-fitting procedure to avoid direct conditional outcome distribution estimation.
- The estimator is consistent, asymptotically normal, and achieves parametric convergence rates.
Why it matters
Understanding causal effects in networks with interference is crucial for policy and intervention design. This work provides a robust, efficient method to estimate these effects on outcome quantiles, especially useful in clustered settings. It advances causal inference by offering a flexible, data-adaptive approach.
Original 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.
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