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.
Related
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- Local conformal prediction for individual causal effects
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- Pilot-Projected Nonlinear Self-Interference Estimation with Simultaneous Uplink Training
BibTeX
@misc{zecchin2026conformal,
title = {{Conformal Individual Treatment Effect Estimation under Networked Interference}},
author = {Matteo Zecchin and Osvaldo Simeone},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.15254},
eprint = {2609.15254},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.15254},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Conformal Individual Treatment Effect Estimation under Networked Interference
AU - Matteo Zecchin
AU - Osvaldo Simeone
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.15254
UR - https://arxiv.org/abs/2609.15254
AB - 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.
ER -