arXiv 10 May 2022 · Econometrics
arXiv:2205.04637 · PDF · DOI · OpenAlex · Extracted main text
The effects of treatments are often heterogeneous, depending on the observable characteristics, and it is necessary to exploit such heterogeneity to devise individualized treatment rules (ITRs). Existing estimation methods of such ITRs assume that the available experimental or observational data are derived from the target population in which the estimated policy is implemented. However, this assumption often fails in practice because of limited useful data. In this case, policymakers must rely on the data generated in the source population, which differs from the target population. Unfortunately, existing estimation methods do not necessarily work as expected in the new setting, and strategies that can achieve a reasonable goal in such a situation are required. This study examines the application of distributionally robust optimization (DRO), which formalizes an ambiguity about the target population and adapts to the worst-case scenario in the set. It is shown that DRO with Wasserstein distance-based characterization of ambiguity provides simple intuitions and a simple estimation method. I then develop an estimator for the distributionally robust ITR and evaluate its theoretical performance. An empirical application shows that the proposed approach outperforms the naive approach in the target population.
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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 | Kitagawa, T. and Tetenov, A (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 1.000 | 6 | 3 | 100% |
| 2 | Hotz, V. J., Imbens, G. W., and Mortimer, J. H (2005) Predicting the efficacy of future training programs using past experiences at other locations | 0.928 | 4 | 3 | 100% |
| 3 | Zhou, Z., Athey, S., and Wager, S (2022) Offline Multi-Action Policy Learning: Generalization and Optimization | 0.928 | 4 | 3 | 100% |
| 4 | Mo, W., Qi, Z., and Liu, Y (2021) Learning Optimal Distributionally Robust Individualized Treatment Rules | 0.874 | 8 | 2 | 100% |
| 5 | Zhao, Y., Zeng, D., Tangen, C. M., and Leblanc, M. L (2019) Robustifying trial-derived optimal treatment rules for a target population | 0.874 | 6 | 2 | 100% |
| 6 | Si, N., Zhang, F., Zhou, Z., and Blanchet, J (2021) Distributional Robust Batch Contextual Bandits | 0.811 | 4 | 2 | 100% |
| 7 | Uehara, M., Kato, M., and Yasui, S (2020) Off-Policy Evaluation and Learning for External Validity under a Covariate Shift | 0.737 | 3 | 2 | 100% |
| 8 | Adjaho, C. and Christensen, T (2022) Externally Valid Treatment Choice | 0.644 | 4 | 1 | 100% |
| 9 | Blanchet, J. and Murthy, K (2019) Quantifying Distributional Model Risk via Optimal Transport | 0.585 | 3 | 1 | 100% |
| 10 | Si, N., Zhang, F., Zhou, Z., and Blanchet, J (2020) Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits | 0.585 | 3 | 1 | 100% |
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