mlsanityData to 7 Oct 2026

An Efficient Doubly-Robust Test for the Kernel Treatment Effect

arXiv 2304.13237 ↗First posted 26 Apr 2023stat.ME stat.MLcited by 10

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Abstract (arXiv)

The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects beyond the mean, for instance decreasing or increasing the variance. We propose a new kernel-based test for distributional effects of the treatment. It is, to the best of our knowledge, the first kernel-based, doubly-robust test with provably valid type-I error. Furthermore, our proposed algorithm is computationally efficient, avoiding the use of permutations.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

24 of 57 references.

References · 24

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  3. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 360
  4. 2016Learning Representations for Counterfactual Inferencecited by 327
  5. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 129
  6. 2019Kernel Instrumental Variable Regressioncited by 118
  7. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  8. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 62
  9. 2022Optimal Rates for Regularized Conditional Mean Embedding Learningcited by 50
  10. 2021Semiparametric counterfactual density estimationcited by 42
  11. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  12. 2020Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regressioncited by 36
12 more
  1. 2020Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curvescited by 36
  2. 2018Counterfactual Mean Embeddingscited by 32
  3. 2017Nonparametric causal effects based on incremental propensity score interventionscited by 28
  4. 2018Sharp instruments for classifying compliers and generalizing causal effectscited by 22
  5. 2022A Permutation-free Kernel Two-Sample Testcited by 21
  6. 2018Causal effects based on distributional distancescited by 15
  7. 2020Dimension-agnostic inference using cross U-statisticscited by 14
  8. 2015Nonparametric Bounds and Sensitivity Analysis of Treatment Effectscited by 12
  9. 2022Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approachcited by 9
  10. 2015An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functionscited by 8
  11. 2015Efficient Estimation of Quantiles in Missing Data Modelscited by 5
  12. 2022Doubly Robust Kernel Statistics for Testing Distributional Treatment Effectscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 2.

10 papers.

  1. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  2. 2025DoubleGen: Debiased Generative Modeling of Counterfactualscited by 6
  3. 2025Doubly-Robust Estimation of Counterfactual Policy Mean Embeddingscited by 3
  4. 2025Locally minimax optimal confidence sets for the best modelcited by 2
  5. 2025A Two-armed Bandit Framework for A/B Testingcited by 2
  6. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
  7. 2025Kernel Treatment Effects with Adaptively Collected Datacited by 1
  8. 2026Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testingcited by 1
  9. 2025Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spacescited by 0
  10. 2026Wasserstein Causal Forests for Distribution-Valued Outcomescited by 0