mlsanityData to 14 Oct 2026

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

arXiv 1712.04912 ↗First posted 13 Dec 2017stat.ML econ.EM math.STcited by 332

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Abstract (arXiv)

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estimate marginal effects and treatment propensities in order to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods. From a practical perspective, our method is flexible and easy to use: In both steps, we can use any loss-minimization method, e.g., penalized regression, deep neural networks, or boosting; moreover, these methods can be fine-tuned by cross validation. Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property: Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same error bounds as an oracle who has a priori knowledge of these two nuisance components. We implement variants of our approach based on penalized regression, kernel ridge regression, and boosting in a variety of simulation setups, and find promising performance relative to existing baselines.

Key facts. Cited by 332 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 30; 2022: 41; 2023: 66; 2024: 66; 2025: 71; 2026: 58.

332 papers.

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  1. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  2. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 75
  3. 2024Causal machine learning for predicting treatment outcomescited by 74
  4. 2025Do-PFN: In-Context Learning for Causal Effect Estimationcited by 51
  5. 2023A Survey on Causal Reinforcement Learningcited by 45
  6. 2023Spawrious: A Benchmark for Fine Control of Spurious Correlation Biasescited by 41
  7. 2021Causal Effect Inference for Structured Treatmentscited by 39
  8. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  9. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  10. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 32
  11. 2021Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effectscited by 31
  12. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
38 more
  1. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  2. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  3. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  4. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  5. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  6. 2023Explicit Feature Interaction-aware Uplift Network for Online Marketingcited by 25
  7. 2021Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detectioncited by 21
  8. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  9. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  10. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  11. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  12. 2022Image-based Treatment Effect Heterogeneitycited by 20
  13. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  14. 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 20
  15. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  16. 2024Model-agnostic meta-learners for estimating heterogeneous treatment effects over timecited by 19
  17. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
  18. 2021Machine Learning for Variance Reduction in Online Experimentscited by 17
  19. 2023Zero-shot causal learningcited by 17
  20. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  21. 2023Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencodercited by 17
  22. 2024DiffPO: A causal diffusion model for learning distributions of potential outcomescited by 17
  23. 2023Causal isotonic calibration for heterogeneous treatment effectscited by 16
  24. 2022What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatmentcited by 15
  25. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  26. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  27. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  28. 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
  29. 2022Learning from a Biased Samplecited by 14
  30. 2025TabPFN: One Model to Rule Them All?cited by 14
  31. 2021A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modelingcited by 12
  32. 2022Treatment Effect Risk: Bounds and Inferencecited by 12
  33. 2025Doubly Robust Alignment for Large Language Modelscited by 12
  34. 2023One-Step Estimation of Differentiable Hilbert-Valued Parameterscited by 11
  35. 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
  36. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  37. 2021Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessmentcited by 10
  38. 2022Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizonscited by 10

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