Tatsushi Oka, Shota Yasui, Yuta Hayakawa, Undral Byambadalai
arXiv 19 Jul 2024 · Econometrics · publishedEconometric Reviews (2025) · 3 citations (OpenAlex)
arXiv:2407.14074 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we address the issue of estimating and inferring distributional treatment effects in randomized experiments. The distributional treatment effect provides a more comprehensive understanding of treatment heterogeneity compared to average treatment effects. We propose a regression adjustment method that utilizes distributional regression and pre-treatment information, establishing theoretical efficiency gains without imposing restrictive distributional assumptions. We develop a practical inferential framework and demonstrate its advantages through extensive simulations. Analyzing water conservation policies, our method reveals that behavioral nudges systematically shift consumption from high to moderate levels. Examining health insurance coverage, we show the treatment reduces the probability of zero doctor visits by 6.6 percentage points while increasing the likelihood of 3-6 visits. In both applications, our regression adjustment method substantially improves precision and identifies treatment effects that were statistically insignificant under conventional approaches.
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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 | Negi, A. and J. M. Wooldridge (2020) Robust and efficient estimation of potential outcome means under random assignment | 0.737 | 3 | 2 | 100% |
| 2 | Neyman, J (1923) On the application of probability theory to agricultural experiments. essay on principles. section 9. trans. dorota m. dabrowska… | 0.644 | 2 | 2 | 100% |
| 3 | Heckman, J. J., J. Smith, and N. Clements (1997) Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts | 0.644 | 2 | 2 | 100% |
| 4 | List, J. A., I. Muir, and G. K. Sun (2022) Using machine learning for efficient flexible regression adjustment in economic experiments | 0.644 | 2 | 2 | 100% |
| 5 | Negi, A. and J. M. Wooldridge (2021) Revisiting regression adjustment in experiments with heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 6 | Jiang, L., P. C. Phillips, Y. Tao, and Y. Zhang (2023) Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations | 0.585 | 3 | 1 | 100% |
| 7 | McCullagh, P. and J. Nelder (1989) Binary data | 0.511 | 2 | 2 | 50% |
| 8 | Athey, S. and G. Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.511 | 2 | 1 | 100% |
| 9 | Wager, S. and S. Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.511 | 2 | 1 | 100% |
| 10 | Bitler, M. P., J. B. Gelbach, and H. W. Hoynes (2006) What mean impacts miss: Distributional effects of welfare reform experiments | 0.511 | 2 | 1 | 100% |
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