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Preference-based Conditional Treatment Effects and Policy Learning

arXiv 2602.03823 ↗First posted 3 Feb 2026stat.ML cs.LGcited by 0

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Abstract (arXiv)

We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CPTE requires only that outcomes be ranked under a preference rule, unlocking flexible modeling of heterogeneous effects with multivariate, ordinal, or preference-driven outcomes. This unifies applications such as conditional probability of necessity and sufficiency, conditional Win Ratio, and Generalized Pairwise Comparisons. Despite the intrinsic non-identifiability of comparison-based estimands, CPTE provides interpretable targets and delivers new identifiability conditions for previous unidentifiable estimands. We present estimation strategies via matching, quantile, and distributional regression, and further design efficient influence-function estimators to correct plug-in bias and maximize policy value. Synthetic and semi-synthetic experiments demonstrate clear performance gains and practical impact.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 62 references that are in the map. First posted on arXiv 3 Feb 2026.

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References · 24

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  3. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  4. 2016Generalized Random Forestscited by 309
  5. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  6. 2013Probabilities of Causation: Bounds and Identificationcited by 89
  7. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  8. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  9. 2022Personalized Decision Making -- A Conceptual Introductioncited by 29
  10. 2022Probabilities of Causation with Nonbinary Treatment and Effectcited by 24
  11. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  12. 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
12 more
  1. 2023Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?cited by 7
  2. 2022Double Machine Learning Methods for Estimating Average Treatment Effects: A Comparative Studycited by 6
  3. 2023Policy Learning with Distributional Welfarecited by 5
  4. 2024Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forestcited by 5
  5. 2015Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Boundscited by 4
  6. 2020Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation detailscited by 4
  7. 2021Causal effect on a target population: a sensitivity analysis to handle missing covariatescited by 4
  8. 2025Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysiscited by 3
  9. 2025Estimating Probabilities of Causation with Machine Learning Modelscited by 2
  10. 2025The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inferencecited by 2
  11. 2025Optimal treatment regimes for the net benefit of a treatmentcited by 1
  12. 2025Learning Probabilities of Causation with Mask-Augmented Datacited by 1

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