Beyond the Average: Distributional Causal Inference under Imperfect Compliance
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Abstract (arXiv)
We study the estimation of distributional treatment effects in randomized experiments with imperfect compliance. When participants do not adhere to their assigned treatments, we leverage treatment assignment as an instrumental variable to identify the local distributional treatment effect-the difference in outcome distributions between treatment and control groups for the subpopulation of compliers. We propose a regression-adjusted estimator based on a distribution regression framework with Neyman-orthogonal moment conditions, enabling robustness and flexibility with high-dimensional covariates. Our approach accommodates continuous, discrete, and mixed discrete-continuous outcomes, and applies under a broad class of covariate-adaptive randomization schemes, including stratified block designs and simple random sampling. We derive the estimator's asymptotic distribution and show that it achieves the semiparametric efficiency bound. Simulation results demonstrate favorable finite-sample performance, and we demonstrate the method's practical relevance in an application to the Oregon Health Insurance Experiment.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 86 references that are in the map. First posted on arXiv 19 Sep 2025.
Builds on
23 of 86 references.
References · 23
- 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
- 2015The Influence Function of Semiparametric Estimatorscited by 36
- 2013Program Evaluation and Causal Inference with High-Dimensional Datacited by 29
- 2019Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyondcited by 23
- 2022Robust and Agnostic Learning of Conditional Distributional Treatment Effectscited by 18
- 2020Distributional synthetic controlscited by 9
- 2016Decomposing Treatment Effect Variationcited by 8
- 2021Estimating Potential Outcome Distributions with Collaborating Causal Networkscited by 8
- 2023When is the estimated propensity score better? High-dimensional analysis and bias correctioncited by 7
- 2025On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomizationcited by 6
- 2024Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trialscited by 5
- 2024Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reductioncited by 5
11 more
- 2016Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomescited by 4
- 2017Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with only Two Time Periodscited by 4
- 2021Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizationscited by 4
- 2023Covariate Adjustment in Stratified Experimentscited by 3
- 2024Instrumental Variable Estimation of Distributional Causal Effectscited by 3
- 2023A General Form of Covariate Adjustment in Randomized Clinical Trialscited by 2
- 2024Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instrumentscited by 2
- 2022Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliancecited by 1
- 2023A decorrelation method for general regression adjustment in randomized experimentscited by 1
- 2023A unified framework for covariate adjustment under stratified randomizationcited by 1
- 2025Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networkscited by 1
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Citing papers per year: 2026: 2.
2 papers.