[2604.03772] Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding

Statistics > Machine Learning arXiv:2604.03772 (stat) [Submitted on 4 Apr 2026] Title:Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding Authors:Keith Barnatchez, Kevin P. Josey, Rachel C. Nethery, Giovanni Parmigiani View a PDF of the paper titled Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding, by Keith Barnatchez and 3 other authors View PDF HTML (experimental) Abstract:Data-driven decision making frequently relies on predicting counterfactual outcomes. In practice, researchers commonly train counterfactual prediction models on a source dataset to inform decisions on a possibly separate target population. Conformal prediction has arisen as a popular method for producing assumption-lean prediction intervals for counterfactual outcomes that would arise under different treatment decisions in the target population of interest. However, existing methods require that every confounding factor of the treatment-outcome relationship used for training on the source data is additionally measured in the target population, risking miscoverage if important confounders are unmeasured in the target population. In this paper, we introduce a computationally efficient debiased machine learning framework that allows for valid prediction intervals when only a subset of confounders is measured in the target population, a common challenge referred to as runtime confounding. Grounded in ...