Yi Zhang, Eli Ben-Michael, Kosuke Imai
arXiv 29 Aug 2022 · Statistics — Methodology · 4 citations (OpenAlex)
arXiv:2208.13323 · PDF · DOI · OpenAlex · Extracted main text
The regression discontinuity (RD) design is widely used for program evaluation with observational data. The primary focus of the existing literature has been the estimation of the local average treatment effect at the existing treatment cutoff. In contrast, we consider policy learning under the RD design. Because the treatment assignment mechanism is deterministic, learning better treatment cutoffs requires extrapolation. We develop a robust optimization approach to finding optimal treatment cutoffs that improve upon the existing ones. We first decompose the expected utility into point-identifiable and unidentifiable components. We then propose an efficient doubly-robust estimator for the identifiable parts. To account for the unidentifiable components, we leverage the existence of multiple cutoffs that are common under the RD design. Specifically, we assume that the heterogeneity in the conditional expectations of potential outcomes across different groups vary smoothly along the running variable. Under this assumption, we minimize the worst case utility loss relative to the status quo policy. The resulting new treatment cutoffs have a safety guarantee that they will not yield a worse overall outcome than the existing cutoffs. Finally, we establish the asymptotic regret bounds for the learned policy using semi-parametric efficiency theory. We apply the proposed methodology to empirical and simulated data sets.
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 | Cattaneo, M. D., L. Keele, R. Titiunik, and G. Vazquez-Bare (2020) Extrapolating treatment effects in multi-cutoff regression discontinuity designs | 0.961 | 9 | 5 | 89% |
| 2 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.956 | 8 | 5 | 88% |
| 3 | Calonico, S., M. D. Cattaneo, and M. H. Farrell (2018) On the effect of bias estimation on coverage accuracy in nonparametric inference | 0.928 | 5 | 5 | 80% |
| 4 | Kallus, N. and A. Zhou (2018) Confounding-robust policy improvement | 0.928 | 5 | 3 | 80% |
| 5 | Kitagawa, T. and A. Tetenov (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 0.843 | 4 | 4 | 75% |
| 6 | Dowd, C (2021) Donuts and distant cates: Derivative bounds for rd extrapolation | 0.843 | 4 | 4 | 75% |
| 7 | Dong, Y. and A. Lewbel (2015) Identifying the effect of changing the policy threshold in regression discontinuity models | 0.843 | 4 | 3 | 75% |
| 8 | Hahn, J., P. Todd, and W. Van der Klaauw (2001) Identification and estimation of treatment effects with a regression-discontinuity design | 0.843 | 4 | 3 | 75% |
| 9 | Melguizo, T., F. Sanchez, and T. Velasco (2016) Credit for low-income students and access to and academic performance in higher education in colombia: A regression discontinuit… | 0.811 | 4 | 2 | 100% |
| 10 | Ben-Michael, E., D. J. Greiner, K. Imai, and Z. Jiang (2021) Safe policy learning through extrapolation: Application to pre-trial risk assessment self | 0.763 | 9 | 5 | 44% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Learning with New Treatments | 0.585 | 3 | 1 |
| 2 | Individualized Policy Evaluation and Learning under Clustered Network Interference | 0.405 | 1 | 1 |
| 3 | Partial identification via conditional linear programs: estimation and policy learning | 0.405 | 1 | 1 |