Undral Byambadalai, Tatsushi Oka, Shota Yasui
arXiv 22 Jul 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2407.16037 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel regression adjustment method designed for estimating distributional treatment effect parameters in randomized experiments. Randomized experiments have been extensively used to estimate treatment effects in various scientific fields. However, to gain deeper insights, it is essential to estimate distributional treatment effects rather than relying solely on average effects. Our approach incorporates pre-treatment covariates into a distributional regression framework, utilizing machine learning techniques to improve the precision of distributional treatment effect estimators. The proposed approach can be readily implemented with off-the-shelf machine learning methods and remains valid as long as the nuisance components are reasonably well estimated. Also, we establish the asymptotic properties of the proposed estimator and present a uniformly valid inference method. Through simulation results and real data analysis, we demonstrate the effectiveness of integrating machine learning techniques in reducing the variance of distributional treatment effect estimators in finite samples.
appendix boundary found by appendix_command · 34% of the source is main text. Read the extracted text to check this.
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 | 1.000 | 6 | 3 | 100% |
| 2 | List, J. A., Muir, I., and Sun, G. K (2022) Using machine learning for efficient flexible regression adjustment in economic experiments | 0.843 | 3 | 3 | 100% |
| 3 | Chernozhukov, V., Fernández-Val, I., and Melly, B (2013) Inference on counterfactual distributions | 0.737 | 3 | 3 | 67% |
| 4 | Ferraro, P. J. and Price, M. K (2013) Using nonpecuniary strategies to influence behavior: Evidence from a large-scale field experiment | 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 | 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% |
| 7 | Ichimura, H. and Newey, W. K (2022) The influence function of semiparametric estimators | 0.644 | 2 | 2 | 100% |
| 8 | Imbens, G. W. and Rubin, D. B (2015) Causal inference in statistics, social, and biomedical sciences | 0.644 | 2 | 2 | 100% |
| 9 | Neyman, J (1959) Optimal asymptotic tests of composite hypotheses | 0.644 | 2 | 2 | 100% |
| 10 | Robins, J. M. and Rotnitzky, A (1995) Semiparametric efficiency in multivariate regression models with missing data | 0.644 | 2 | 2 | 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.