# Nonparametric efficient inference for network quantile causal effects under partial interference

arXiv: https://arxiv.org/abs/2604.13008 · HTML: https://mlsanity.com/paper/2604.13008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Identification and estimation of treatment and interference effects in observational studies on networks](https://mlsanity.com/paper/1609.06245/) (2016)
- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [Nonparametric causal effects based on incremental propensity score interventions](https://mlsanity.com/paper/1704.00211/) (2017)
- [Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond](https://mlsanity.com/paper/1912.12945/) (2019)
- [Smoothed Quantile Regression with Large-Scale Inference](https://mlsanity.com/paper/2012.05187/) (2020)
- [Uniformly Valid Post-Regularization Confidence Regions for Many Functional Parameters in Z-Estimation Framework](https://mlsanity.com/paper/1512.07619/) (2015)
- [Average Direct and Indirect Causal Effects under Interference](https://mlsanity.com/paper/2104.03802/) (2021)
- [Practical considerations for specifying a super learner](https://mlsanity.com/paper/2204.06139/) (2022)
- [Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference](https://mlsanity.com/paper/2107.12420/) (2021)
- [Doubly Robust Estimation in Observational Studies with Partial Interference](https://mlsanity.com/paper/1806.07422/) (2018)
- [Doubly robust estimation and sensitivity analysis for marginal structural quantile models](https://mlsanity.com/paper/2210.04100/) (2022)
- [Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning](https://mlsanity.com/paper/2307.01049/) (2023)
- [Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interference](https://mlsanity.com/paper/2004.08950/) (2020)
- [Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference](https://mlsanity.com/paper/2212.10959/) (2022)
- [Inverting estimating equations for causal inference on quantiles](https://mlsanity.com/paper/2401.00987/) (2024)
- [Semiparametric causal mediation analysis of cluster-randomized trials for indirect and spillover effects](https://mlsanity.com/paper/2404.18256/) (2024)
- [Nonparametric Causal Survival Analysis with Clustered Interference](https://mlsanity.com/paper/2409.13190/) (2024)

## 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.