Nonparametric efficient inference for network quantile causal effects under partial interference

2026-04-14

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

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Probabilistic Inference
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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

• partial interference
• network quantile causal effects
• nonparametric inference
• causal inference
• clustered data

Innovation

• nonparametric efficiency
• network quantile causal effects
• partial interference
• cross-fitting
• nuisance functions