Takuya Ishihara, Daisuke Kurisu
arXiv 31 Oct 2022 · Econometrics · publishedJournal of Econometrics (2025)
arXiv:2210.17063 · PDF · DOI · OpenAlex · Extracted main text
This study examines the problem of determining whether to treat individuals based on observed covariates. The most common decision rule is the conditional empirical success (CES) rule proposed by Manski (2004), which assigns individuals to treatments that yield the best experimental outcomes conditional on the observed covariates. Conversely, using shrinkage estimators, which shrink unbiased but noisy preliminary estimates toward the average of these estimates, is a common approach in statistical estimation problems because it is well-known that shrinkage estimators may have smaller mean squared errors than unshrunk estimators. Inspired by this idea, we propose a computationally tractable shrinkage rule that selects the shrinkage factor by minimizing an upper bound of the maximum regret. Then, we compare the maximum regret of the proposed shrinkage rule with those of the CES and pooling rules when the space of conditional average treatment effects (CATEs) is correctly specified or misspecified. Our theoretical results demonstrate that the shrinkage rule performs well in many cases and these findings are further supported by numerical experiments. Specifically, we show that the maximum regret of the shrinkage rule can be strictly smaller than those of the CES and pooling rules in certain cases when the space of CATEs is correctly specified. In addition, we find that the shrinkage rule is robust against misspecification of the space of CATEs. Finally, we apply our method to experimental data from the National Job Training Partnership Act Study.
appendix boundary found by appendix_titled_section at “Appendix A. Proofs and lemmas” · 74% 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 | Stoye, J (2012) Minimax regret treatment choice with covariates or with limited validity of experiments | 1.000 | 7 | 3 | 100% |
| 2 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 1.000 | 5 | 3 | 100% |
| 3 | Ishihara, T. and T. Kitagawa (2024) Evidence Aggregation for Treatment Choice self | 0.843 | 5 | 3 | 60% |
| 4 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.737 | 3 | 2 | 100% |
| 5 | Tetenov, A (2012) Statistical treatment choice based on asymmetric minimax regret criteria | 0.737 | 3 | 2 | 100% |
| 6 | Armstrong, T. B. and M. Kolesár (2018) Optimal inference in a class of regression models | 0.644 | 2 | 2 | 100% |
| 7 | Armstrong, T. B. and M. Kolesár (2021) Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness | 0.644 | 2 | 2 | 100% |
| 8 | Montiel Olea, J., C. Qiu, and J. Stoye (2025) Decision Theory for Treatment Choice Problems with Partial Identification | 0.644 | 2 | 2 | 100% |
| 9 | Yata, K (2025) Optimal Decision Rules Under Partial Identification | 0.644 | 2 | 2 | 100% |
| 10 | Xie, X., S. Kou, and L. D. Brown (2012) SURE estimates for a heteroscedastic hierarchical model | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 16 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Compound Selection Decisions: An Almost SURE Approach | 0.405 | 1 | 1 |