mlsanityData to 14 Oct 2026

On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization

arXiv 2506.05945 ↗First posted 6 Jun 2025econ.EM math.ST stat.MLcited by 6

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Abstract (arXiv)

This paper focuses on the estimation of distributional treatment effects in randomized experiments that use covariate-adaptive randomization (CAR). These include designs such as Efron's biased-coin design and stratified block randomization, where participants are first grouped into strata based on baseline covariates and assigned treatments within each stratum to ensure balance across groups. In practice, datasets often contain additional covariates beyond the strata indicators. We propose a flexible distribution regression framework that leverages off-the-shelf machine learning methods to incorporate these additional covariates, enhancing the precision of distributional treatment effect estimates. We establish the asymptotic distribution of the proposed estimator and introduce a valid inference procedure. Furthermore, we derive the semiparametric efficiency bound for distributional treatment effects under CAR and demonstrate that our regression-adjusted estimator attains this bound. Simulation studies and empirical analyses of microcredit programs highlight the practical advantages of our method.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 88 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

25 of 88 references.

References · 25

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 332
  3. 2016Learning Representations for Counterfactual Inferencecited by 329
  4. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  5. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
  6. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 158
  7. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  8. 2015The Influence Function of Semiparametric Estimatorscited by 36
  9. 2013Program Evaluation and Causal Inference with High-Dimensional Datacited by 29
  10. 2019Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyondcited by 23
  11. 2014Designing and Deploying Online Field Experimentscited by 18
  12. 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
13 more
  1. 2021Machine Learning for Variance Reduction in Online Experimentscited by 17
  2. 2023Causal isotonic calibration for heterogeneous treatment effectscited by 16
  3. 2021Estimating Potential Outcome Distributions with Collaborating Causal Networkscited by 8
  4. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  5. 2024Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trialscited by 5
  6. 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
  7. 2024Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forestcited by 5
  8. 2016Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomescited by 4
  9. 2017Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with only Two Time Periodscited by 4
  10. 2021Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizationscited by 4
  11. 2023Covariate Adjustment in Stratified Experimentscited by 3
  12. 2018Inference under Covariate-Adaptive Randomization with Multiple Treatmentscited by 2
  13. 2023A General Form of Covariate Adjustment in Randomized Clinical Trialscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliancecited by 2
  2. 2025Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networkscited by 1
  3. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
  4. 2026Fast Rerandomization for Balancing Covariates in Randomized Experiments: A Metropolis-Hastings Frameworkcited by 0
  5. 2026Distributional Causal Mediation via Conditional Generative Modelingcited by 0
  6. 2026Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experimentscited by 0