Undral Byambadalai, Tomu Hirata, Tatsushi Oka, Shota Yasui
arXiv 6 Jun 2025 · Econometrics · 2 citations (OpenAlex)
arXiv:2506.05945 · PDF · DOI · OpenAlex · Extracted main text
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.
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| 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 | 1.000 | 5 | 3 | 100% |
| 2 | Imbens, G. W. and Rubin, D. B (2015) Causal inference in statistics, social, and biomedical sciences | 0.737 | 3 | 2 | 100% |
| 3 | Jiang, L., Phillips, P. C., Tao, Y., and Zhang, Y (2023) Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations | 0.659 | 7 | 3 | 29% |
| 4 | Attanasio, O., Augsburg, B., De Haas, R., Fitzsimons, E., and Harmga… (2015) The impacts of microfinance: Evidence from joint-liability lending in mongolia | 0.644 | 3 | 2 | 67% |
| 5 | Chernozhukov, V., Escanciano, J. C., Ichimura, H., Newey, W. K., and… (2022) Locally robust semiparametric estimation | 0.644 | 2 | 2 | 100% |
| 6 | Cytrynbaum, M (2024) Covariate adjustment in stratified experiments | 0.644 | 2 | 2 | 100% |
| 7 | Freedman, D. A (2008) On regression adjustments to experimental data | 0.644 | 2 | 2 | 100% |
| 8 | Heckman, J. J., Smith, J., and Clements, N (1997) Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts | 0.644 | 2 | 2 | 100% |
| 9 | Lin, W (2013) Agnostic notes on regression adjustments to experimental data: Reexamining freedman’s critique | 0.644 | 2 | 2 | 100% |
| 10 | Näf, J. and Susmann, H (2024) Causal-drf: Conditional kernel treatment effect estimation using distributional random forest | 0.644 | 2 | 2 | 100% |
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