arXiv 27 Nov 2023 · Statistics — Methodology · publishedJournal of the American Statistical Association (2025) · 1 citations (OpenAlex)
arXiv:2311.15878 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we explore optimal treatment allocation policies that target distributional welfare. Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE). While average welfare is intuitive, it may yield undesirable allocations especially when individuals are heterogeneous (e.g., with outliers) - the very reason individualized treatments were introduced in the first place. This observation motivates us to propose an optimal policy that allocates the treatment based on the conditional quantile of individual treatment effects (QoTE). Depending on the choice of the quantile probability, this criterion can accommodate a policymaker who is either prudent or negligent. The challenge of identifying the QoTE lies in its requirement for knowledge of the joint distribution of the counterfactual outcomes, which is not generally point-identified. We introduce minimax policies that are robust to this model uncertainty. A range of identifying assumptions can be used to yield more informative policies. For both stochastic and deterministic policies, we establish the asymptotic bound on the regret of implementing the proposed policies. The framework can be generalized to any setting where welfare is defined as a functional of the joint distribution of the potential outcomes.
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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 | Zhao, Y., D. Zeng, A. J. Rush, and M. R. Kosorok (2012) Estimating individualized treatment rules using outcome weighted learning | 0.928 | 5 | 4 | 80% |
| 2 | Leqi, L. and E. H. Kennedy (2021) Median optimal treatment regimes | 0.874 | 5 | 2 | 100% |
| 3 | Cui, Y (2021) Individualized decision making under partial identification: three perspectives, two optimality results, and one paradox self | 0.737 | 3 | 2 | 100% |
| 4 | Wang, L., Y. Zhou, R. Song, and B. Sherwood (2018) Quantile-optimal treatment regimes | 0.737 | 3 | 2 | 100% |
| 5 | Blundell, R., A. Gosling, H. Ichimura, and C. Meghir (2007) Changes in the distribution of male and female wages accounting for employment composition using bounds | 0.644 | 2 | 2 | 100% |
| 6 | D'Adamo, R (2021) Orthogonal Policy Learning Under Ambiguity | 0.644 | 2 | 2 | 100% |
| 7 | Frandsen, B. R. and L. J. Lefgren (2021) Partial identification of the distribution of treatment effects with an application to the Knowledge is Power Program (KIPP) | 0.644 | 2 | 2 | 100% |
| 8 | Han, S (2023) Optimal dynamic treatment regimes and partial welfare ordering self | 0.644 | 2 | 2 | 100% |
| 9 | Hirano, K. and G. W. Imbens (2001) Estimation of causal effects using propensity score weighting: An application to data on right heart catheterization | 0.644 | 2 | 2 | 100% |
| 10 | Manski, C. F (2007) Minimax-regret treatment choice with missing outcome data | 0.644 | 2 | 2 | 100% |
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