mlsanityData to 7 Oct 2026

Causal isotonic calibration for heterogeneous treatment effects

arXiv 2302.14011 ↗First posted 27 Feb 2023stat.ML cs.LG stat.MEcited by 16

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Abstract (arXiv)

We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold-out calibration sets. Cross-calibration leverages cross-fitted predictors and generates a single calibrated predictor using all available data. Under weak conditions that do not assume monotonicity, we establish that both causal isotonic calibration and cross-calibration achieve fast doubly-robust calibration rates, as long as either the propensity score or outcome regression is estimated accurately in a suitable sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm, providing robust and distribution-free calibration guarantees while preserving predictive performance.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 18 of its 81 references that are in the map. First posted on arXiv 27 Feb 2023.

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18 of 81 references.

References · 18

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  4. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  5. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  6. 2019Orthogonal Statistical Learningcited by 150
  7. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
  8. 2020Uncertainty Quantification and Deep Ensemblescited by 138
  9. 2020An Introduction to Proximal Causal Learningcited by 91
  10. 2020Distribution-free binary classification: prediction sets, confidence intervals and calibrationcited by 79
  11. 2019Estimating Treatment Effects with Causal Forests: An Applicationcited by 58
  12. 2021Distribution-free calibration guarantees for histogram binning without sample splittingcited by 44
6 more
  1. 2019Machine Learning Estimation of Heterogeneous Treatment Effects with Instrumentscited by 29
  2. 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 29
  3. 2020Stable discovery of interpretable subgroups via calibration in causal studiescited by 18
  4. 2022Calibration Error for Heterogeneous Treatment Effectscited by 5
  5. 2020From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome frameworkcited by 2
  6. 2020A Calibration Approach to Transportability and Data-Fusion with Observational Datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 5; 2026: 3.

16 papers.

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  1. 2024Self-Calibrating Conformal Predictioncited by 16
  2. 2024Stabilized Inverse Probability Weighting via Isotonic Calibrationcited by 8
  3. 2025Generalized Venn and Venn-Abers Calibration with Applications in Conformal Predictioncited by 6
  4. 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
  5. 2024Orthogonal Causal Calibrationcited by 5
  6. 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
  7. 2025Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimandscited by 5
  8. 2024Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibrationcited by 4
  9. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  10. 2023Calibrated and Conformal Propensity Scores for Causal Effect Estimationcited by 3
  11. 2024Distributionally robust risk evaluation with an isotonic constraintcited by 3
  12. 2025Bellman Calibration for V-Learning in Offline Reinforcement Learningcited by 2
4 more
  1. 2024Robust CATE Estimation Using Novel Ensemble Methodscited by 0
  2. 2026Attenuated Heterogeneity in Fixed-Effects Causal Forests, and a Cross-Fitted Correctioncited by 0
  3. 2026An analysis of binary isotonic regression: degrees of freedom and implications for calibrationcited by 0
  4. 2026Bellman Calibration for Marginalized Importance Weighting in Offline Reinforcement Learningcited by 0