A Systematic Review of Conformal Inference Procedures for Treatment Effect Estimation: Methods and Challenges
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Abstract (arXiv)
Treatment effect estimation is essential for informed decision-making in many fields such as healthcare, economics, and public policy. While flexible machine learning models have been widely applied for estimating heterogeneous treatment effects, quantifying the inherent uncertainty of their point predictions remains an issue. Recent advancements in conformal prediction address this limitation by allowing for inexpensive computation, as well as distribution shifts, while still providing frequentist, finite-sample coverage guarantees under minimal assumptions for any point-predictor model. This advancement holds significant potential for improving decision-making in especially high-stakes environments. In this work, we perform a systematic review regarding conformal prediction methods for treatment effect estimation and provide for both the necessary theoretical background. Through a systematic filtering process, we select and analyze eleven key papers, identifying and describing current state-of-the-art methods in this area. Based on our findings, we propose directions for future research.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 25 Sep 2025.
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25 of 48 references.
References · 25
- 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
- 2016Distribution-Free Predictive Inference For Regressioncited by 668
- 2019Conformalized Quantile Regressioncited by 629
- 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
- 2019Conformal Prediction Under Covariate Shiftcited by 519
- 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
- 2016Generalized Random Forestscited by 312
- 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 151
- 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
- 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
- 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
- 2024Theoretical Foundations of Conformal Predictioncited by 101
13 more
- 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
- 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 42
- 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Valuescited by 36
- 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
- 2020Conformal e-predictioncited by 22
- 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
- 2022Conformal Off-policy Predictioncited by 14
- 2024Conformal Counterfactual Inference under Hidden Confoundingcited by 10
- 2024Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effectscited by 8
- 2025Conformal Inference of Individual Treatment Effects Using Conditional Density Estimatescited by 3
- 2022Undersmoothing Causal Estimators with Generative Treescited by 2
- 2024On the Role of Surrogates in Conformal Inference of Individual Causal Effectscited by 2
- 2024Conformal Diffusion Models for Individual Treatment Effect Estimation and Inferencecited by 1
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Citing papers per year: 2026: 1.
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