Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inference

• URL: http://arxiv.org/abs/2512.08828v1
• Date: Tue, 09 Dec 2025 17:18:09 GMT
• Title: Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inference
• Authors: Swaraj Bose, Walter Dempsey,
• Abstract summary: We propose a novel method for constructing prediction intervals using conformal inference techniques for time-varying ITEs.<n>Although our method is broadly applicable across decision-making contexts, we support our theoretical claims with simulations emulating micro-randomized trials.
• Score: 0.6138671548064355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately quantifying uncertainty of individual treatment effects (ITEs) across multiple decision points is crucial for personalized decision-making in fields such as healthcare, finance, education, and online marketplaces. Previous work has focused on predicting non-causal longitudinal estimands or constructing prediction bands for ITEs using cross-sectional data based on exchangeability assumptions. We propose a novel method for constructing prediction intervals using conformal inference techniques for time-varying ITEs with weaker assumptions than prior literature. We guarantee a lower bound for coverage, which is dependent on the degree of non-exchangeability in the data. Although our method is broadly applicable across decision-making contexts, we support our theoretical claims with simulations emulating micro-randomized trials (MRTs) -- a sequential experimental design for mobile health (mHealth) studies. We demonstrate the practical utility of our method by applying it to a real-world MRT - the Intern Health Study (IHS).

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