Technology Category
Machine Learning: Causal LearningReasoning under Uncertainty: Probabilistic InferenceCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
Nonparametric efficient inference for network quantile causal effects under partial interference
🤖 AI Summary
This study addresses efficient inference of network quantile treatment effects under partial interference—where spillover effects are confined within groups. The authors develop a nonparametric efficient estimation framework that integrates triple cross-fitting to circumvent direct estimation of the conditional outcome distribution and accommodates data-adaptive modeling of the perturbation function. Grounded in nonparametric efficiency theory, the proposed estimator achieves parametric convergence rates and enjoys consistency and asymptotic normality, demonstrating strong finite-sample performance. The method is successfully applied to clustered observational data, offering a theoretically sound and practically viable tool for causal inference in networked settings.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
partial interference
network quantile causal effects
nonparametric inference
causal inference
clustered data
Innovation
Methods, ideas, or system contributions that make the work stand out.
nonparametric efficiency
network quantile causal effects
partial interference
cross-fitting
nuisance functions
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