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Reliable Selection of Heterogeneous Treatment Effect Estimators

arXiv 2511.18464 ↗First posted 23 Nov 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

We study the problem of selecting the best heterogeneous treatment effect (HTE) estimator from a collection of candidates in settings where the treatment effect is fundamentally unobserved. We cast estimator selection as a multiple testing problem and introduce a ground-truth-free procedure based on a cross-fitted, exponentially weighted test statistic. A key component of our method is a two-way sample splitting scheme that decouples nuisance estimation from weight learning and ensures the stability required for valid inference. Leveraging a stability-based central limit theorem, we establish asymptotic familywise error rate control under mild regularity conditions. Empirically, our procedure provides reliable error control while substantially reducing false selections compared with commonly used methods across ACIC 2016, IHDP, and Twins benchmarks, demonstrating that our method is feasible and powerful even without ground-truth treatment effects.

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References · 17

  1. 2016Layer Normalizationcited by 5,669
  2. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
  3. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  4. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  5. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  6. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  7. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  8. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  9. 2020RealCause: Realistic Causal Inference Benchmarkingcited by 46
  10. 2022Ranking Inferences Based on the Top Choice of Multiway Comparisonscited by 22
  11. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  12. 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
5 more
  1. 2024Winners with Confidence: Discrete Argmin Inference with an Application to Model Selectioncited by 8
  2. 2025Treatment Effect Estimation for Optimal Decision-Makingcited by 8
  3. 2020Assessment of Heterogeneous Treatment Effect Estimation Accuracy via Matchingcited by 4
  4. 2025Locally minimax optimal confidence sets for the best modelcited by 3
  5. 2025A Relative Error-Based Evaluation Framework of Heterogeneous Treatment Effect Estimatorscited by 1

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