mlsanityData to 14 Oct 2026

Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

arXiv 2306.03625 ↗First posted 6 Jun 2023stat.ME cs.LG stat.MLcited by 6

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Abstract (arXiv)

We propose a simple and general framework for nonparametric estimation of heterogeneous treatment effects under fairness constraints. Under standard regularity conditions, we show that the resulting estimators possess the double robustness property. We use this framework to characterize the trade-off between fairness and the maximum welfare achievable by the optimal policy. We evaluate the methods in a simulation study and illustrate them in a real-world case study.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

26 of 71 references.

References · 26

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  3. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  4. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  5. 2017Algorithmic decision making and the cost of fairnesscited by 403
  6. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  7. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
  8. 2017Fair Inference On Outcomescited by 144
  9. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 133
  10. 2017Double/Debiased/Neyman Machine Learning of Treatment Effectscited by 76
  11. 2019Counterfactual Risk Assessments, Evaluation, and Fairnesscited by 59
  12. 2017Balanced Policy Evaluation and Learningcited by 52
14 more
  1. 2018Learning Optimal Fair Policiescited by 49
  2. 2021Semiparametric counterfactual density estimationcited by 42
  3. 2020Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Oddscited by 40
  4. 2016Covariate Balancing Propensity Score by Tailored Loss Functionscited by 34
  5. 2018Sharp instruments for classifying compliers and generalizing causal effectscited by 22
  6. 2020Counterfactual Predictions under Runtime Confoundingcited by 22
  7. 2018Causal effects based on distributional distancescited by 16
  8. 2017Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effectcited by 15
  9. 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
  10. 2020Fair Policy Targetingcited by 12
  11. 2017Semiparametric theorycited by 7
  12. 2023Doubly Robust Counterfactual Classificationcited by 5
  13. 2017Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admissioncited by 3
  14. 2022Counterfactual Mean-variance Optimizationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2025Treatment Effect Estimation for Optimal Decision-Makingcited by 8
  2. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 5
  3. 2024Causal K-Means Clusteringcited by 3
  4. 2025Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shiftcited by 1
  5. 2025Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rulescited by 0
  6. 2026Learning Optimal Individualized Decision Rules with Conditional Demographic Paritycited by 0