Conformal Individual Treatment Effect Estimation under Networked Interference

2026-09-14

AI Summary

本文解决了网络干扰下的个体治疗效果估计问题，通过开发一种考虑干扰的加权共形预测方法来实现有效的覆盖保证。

Abstract

Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treatment effects under the no-interference assumption. In this work, we relax this assumption by allowing each unit's potential outcomes to depend on other units' treatments and covariates. In this setting, propensity-score reweighting does not restore weighted exchangeability, and existing methods may fail to achieve valid coverage. To address this issue, we develop interference-adjusted weighted conformal prediction that accounts for interference by constructing an observable upper bound on the ideal and unobserved conformal $p$-value under the target intervention. The resulting prediction sets provide finite-sample marginal coverage guarantees for counterfactual outcomes and individual treatment effects in both transductive and inductive settings. We also derive a sharper construction when intervention-induced changes in nonconformity scores are bounded. Numerical experiments show that our methods preserve nominal coverage, whereas existing methods may not.

Problem

• Networked Interference
• Individual Treatment Effect
• Counterfactual Outcomes
• Propensity-Score Reweighting
• Conformal Prediction

Innovation

• interference-adjusted weighted conformal prediction
• networked interference
• finite-sample coverage guarantees