mlsanityData to 7 Oct 2026

Robust and Agnostic Learning of Conditional Distributional Treatment Effects

arXiv 2205.11486 ↗First posted 23 May 2022stat.ML cs.LG econ.EMcited by 18

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Abstract (arXiv)

The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) average, and can overlook risks and tail events, which are important to treatment choice. In aggregate analyses, this is usually addressed by measuring the distributional treatment effect (DTE), such as differences in quantiles or tail expectations between treatment groups. Hypothetically, one can similarly fit conditional quantile regressions in each treatment group and take their difference, but this would not be robust to misspecification or provide agnostic best-in-class predictions. We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a class of problems that includes conditional quantile treatment effects, conditional super-quantile treatment effects, and conditional treatment effects on coherent risk measures given by f-divergences. Our method is based on constructing a special pseudo-outcome and regressing it on covariates using any regression learner. Our method is model-agnostic in that it can provide the best projection of CDTE onto the regression model class. Our method is robust in that even if we learn these nuisances nonparametrically at very slow rates, we can still learn CDTEs at rates that depend on the class complexity and even conduct inferences on linear projections of CDTEs. We investigate the behavior of our proposal in simulations, as well as in a case study of 401(k) eligibility effects on wealth.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 29 of its 72 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

29 of 72 references.

References · 29

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2016Error bounds for approximations with deep ReLU networkscited by 567
  3. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  4. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  5. 2016Learning Representations for Counterfactual Inferencecited by 327
  6. 2016Generalized Random Forestscited by 309
  7. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  8. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  9. 2019Orthogonal Statistical Learningcited by 150
  10. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 129
  11. 2019Deep Generalized Method of Moments for Instrumental Variable Analysiscited by 88
  12. 2019Problem Formulation and Fairnesscited by 85
17 more
  1. 2020Minimax Estimation of Conditional Moment Modelscited by 83
  2. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  3. 2018Orthogonal Random Forest for Causal Inferencecited by 51
  4. 2017Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectscited by 41
  5. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  6. 2015The Influence Function of Semiparametric Estimatorscited by 36
  7. 2013Program Evaluation and Causal Inference with High-Dimensional Datacited by 29
  8. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  9. 2019Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyondcited by 23
  10. 2020The Variational Method of Momentscited by 23
  11. 2015Structural Nested Models and G-estimation: The Partially Realized Promisecited by 15
  12. 2022Treatment Effect Risk: Bounds and Inferencecited by 12
  13. 2021Estimating Potential Outcome Distributions with Collaborating Causal Networkscited by 8
  14. 2021Median Optimal Treatment Regimescited by 7
  15. 2019Non-Parametric Inference Adaptive to Intrinsic Dimensioncited by 4
  16. 2021Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Modelscited by 4
  17. 2018Network and Panel Quantile Effects Via Distribution Regressioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 3; 2025: 4; 2026: 5.

18 papers.

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  1. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  2. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  3. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  4. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  5. 2023Positivity-free Policy Learning with Observational Datacited by 6
  6. 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
  7. 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
  8. 2023Asymptotically Unbiased Synthetic Control Methods by Moment Matchingcited by 2
  9. 2024Conditional Outcome Equivalence: A Quantile Alternative to CATEcited by 2
  10. 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliancecited by 2
  11. 2022Robust Direct Learning for Causal Data Fusioncited by 1
  12. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
6 more
  1. 2026Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testingcited by 1
  2. 2025A fine-grained look at causal effects in causal spacescited by 0
  3. 2026Direct Doubly Robust Estimation of Conditional Quantile Contrastscited by 0
  4. 2026Preference-based Conditional Treatment Effects and Policy Learningcited by 0
  5. 2026Distributional Causal Mediation via Conditional Generative Modelingcited by 0
  6. 2026Wasserstein Causal Forests for Distribution-Valued Outcomescited by 0