mlsanityData to 14 Oct 2026

Nonparametric efficient inference for network quantile causal effects under partial interference

arXiv 2604.13008 ↗First posted 14 Apr 2026stat.ME stat.AP stat.MLcited by 0

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Abstract (arXiv)

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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References · 18

  1. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 133
  2. 2016Identification and estimation of treatment and interference effects in observational studies on networkscited by 44
  3. 2017Causal inference for social network datacited by 31
  4. 2017Nonparametric causal effects based on incremental propensity score interventionscited by 28
  5. 2019Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyondcited by 23
  6. 2020Smoothed Quantile Regression with Large-Scale Inferencecited by 23
  7. 2015Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Frameworkcited by 12
  8. 2021Average Direct and Indirect Causal Effects under Interferencecited by 10
  9. 2022Practical considerations for specifying a super learnercited by 10
  10. 2021Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interferencecited by 6
  11. 2018Doubly Robust Estimation in Observational Studies with Partial Interferencecited by 5
  12. 2022Doubly robust estimation and sensitivity analysis for marginal structural quantile modelscited by 3
6 more
  1. 2023Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learningcited by 3
  2. 2020Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interferencecited by 2
  3. 2022Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interferencecited by 1
  4. 2024Inverting estimating equations for causal inference on quantilescited by 1
  5. 2024Semiparametric causal mediation analysis of cluster-randomized trials for indirect and spillover effectscited by 1
  6. 2024Nonparametric Causal Survival Analysis with Clustered Interferencecited by 1

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