Christopher Adjaho, Timothy Christensen
arXiv 11 May 2022 · Econometrics
arXiv:2205.05561 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of learning personalized treatment policies that are externally valid or generalizable: they perform well in other target populations besides the experimental (or training) population from which data are sampled. We first show that welfare-maximizing policies for the experimental population are robust to shifts in the distribution of outcomes (but not characteristics) between the experimental and target populations. We then develop new methods for learning policies that are robust to shifts in outcomes and characteristics. In doing so, we highlight how treatment effect heterogeneity within the experimental population affects the generalizability of policies. Our methods may be used with experimental or observational data (where treatment is endogenous). Many of our methods can be implemented with linear programming.
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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 | Kido, D (2022) Distributionally robust policy learning with wasserstein distance | 1.000 | 7 | 4 | 100% |
| 2 | Mo, W., Z. Qi, and Y. Liu (2021) Learning optimal distributionally robust individualized treatment rules | 1.000 | 5 | 3 | 100% |
| 3 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.928 | 5 | 4 | 80% |
| 4 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.928 | 4 | 3 | 100% |
| 5 | Spini, P. E (2021) Robustness, heterogeneous treatment effects and covariate shifts | 0.928 | 4 | 3 | 100% |
| 6 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.811 | 4 | 2 | 100% |
| 7 | McKenzie, D. and S. Puerto (2021) Growing markets through business training for female entrepreneurs: A market-level randomized experiment in Kenya | 0.737 | 3 | 2 | 100% |
| 8 | Mbakop, E. and M. Tabord-Meehan (2021) Model selection for treatment choice: Penalized welfare maximization | 0.737 | 3 | 2 | 100% |
| 9 | Qian, M. and S. A. Murphy (2011) Performance guarantees for individualized treatment rules | 0.737 | 3 | 2 | 100% |
| 10 | Si, N., F. Zhang, Z. Zhou, and J. Blanchet (2020) Distributionally robust policy evaluation and learning in offline contextual bandits | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 39 scored citations.
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