Conformal Individual Treatment Effect Estimation under Networked Interference

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.

Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2609.15254 [stat.ML]

(or arXiv:2609.15254v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.15254 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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