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

arXiv 2512.08828 ↗First posted 9 Dec 2025stat.ME stat.MLcited by 0

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Abstract (arXiv)

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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References · 21

  1. 2019Conformalized Quantile Regressioncited by 620
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2019Conformal Prediction Under Covariate Shiftcited by 511
  4. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  5. 2022Conformal prediction beyond exchangeabilitycited by 348
  6. 2022Conformal Risk Controlcited by 290
  7. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  8. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
  9. 2019A comparison of some conformal quantile regression methodscited by 76
  10. 2022Sequential Predictive Conformal Inference for Time Seriescited by 71
  11. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  12. 2016Assessing Time-Varying Causal Effect Moderation in Mobile Healthcited by 27
9 more
  1. 2022Conformal Prediction with Temporal Quantile Adjustmentscited by 25
  2. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  3. 2019Conformal calibratorscited by 22
  4. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  5. 2020Conformal prediction intervals for the individual treatment effectcited by 10
  6. 2022Conformal Prediction Intervals with Temporal Dependencecited by 9
  7. 2024Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effectscited by 7
  8. 2023Conformal Predictions for Longitudinal Datacited by 3
  9. 2024Conformal causal inference for cluster randomized trials: model-robust inference without asymptotic approximationscited by 2

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