Undral Byambadalai, Tomu Hirata, Tatsushi Oka, Shota Yasui
arXiv 19 Sep 2025 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2509.15594 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.928 | 4 | 3 | 100% |
| 2 | Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and… (2022) Locally robust semiparametric estimation | 0.843 | 3 | 3 | 100% |
| 3 | Abadie, A (2002) Bootstrap tests for distributional treatment effects in instrumental variable models | 0.644 | 2 | 2 | 100% |
| 4 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of causal effects using instrumental variables | 0.644 | 2 | 2 | 100% |
| 5 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.644 | 2 | 2 | 100% |
| 6 | Imbens, G. W. and Rubin, D. B (2015) Causal inference in statistics, social, and biomedical sciences | 0.644 | 2 | 2 | 100% |
| 7 | Jiang, L., Phillips, P. C., Tao, Y., and Zhang, Y (2023) Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations | 0.644 | 2 | 2 | 100% |
| 8 | Jiang, L., Linton, O. B., Tang, H., and Zhang, Y (2024) Improving estimation efficiency via regression-adjustment in covariate-adaptive randomizations with imperfect compliance | 0.644 | 2 | 2 | 100% |
| 9 | Robins, J. M. and Rotnitzky, A (1995) Semiparametric efficiency in multivariate regression models with missing data | 0.644 | 2 | 2 | 100% |
| 10 | Abadie, A., Angrist, J., and Imbens, G (2002) Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 84 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments | 0.405 | 1 | 1 |