arXiv 15 Nov 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2311.08963 · PDF · DOI · OpenAlex · Extracted main text
This study investigates the problem of individualizing treatment allocations using stated preferences for treatments. If individuals know in advance how the assignment will be individualized based on their stated preferences, they may state false preferences. We derive an individualized treatment rule (ITR) that maximizes welfare when individuals strategically state their preferences. We also show that the optimal ITR is strategy-proof, that is, individuals do not have a strong incentive to lie even if they know the optimal ITR a priori. Constructing the optimal ITR requires information on the distribution of true preferences and the average treatment effect conditioned on true preferences. In practice, the information must be identified and estimated from the data. As true preferences are hidden information, the identification is not straightforward. We discuss two experimental designs that allow the identification: strictly strategy-proof randomized controlled trials and doubly randomized preference trials. Under the presumption that data comes from one of these experiments, we develop data-dependent procedures for determining ITR, that is, statistical treatment rules (STRs). The maximum regret of the proposed STRs converges to zero at a rate of the square root of the sample size. An empirical application demonstrates our proposed STRs.
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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 | Wing, Coady, Clark, M. H (2017) What Can We Learn From A Doubly Randomized Preference Trial?—An Instrumental Variables Perspective | 1.000 | 7 | 3 | 100% |
| 2 | Kitagawa, Toru, Tetenov, Aleksey (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 1.000 | 5 | 3 | 100% |
| 3 | Manski, Charles F (2004) Statistical Treatment Rules for Heterogeneous Populations | 0.928 | 4 | 3 | 100% |
| 4 | Munro, Evan (2023) Treatment Allocation with Strategic Agents | 0.874 | 5 | 2 | 100% |
| 5 | Wennberg, John E., Barry, Micahel J., Fowler, Floyd J., Mulley, Albert (1993) Outcomes Research, Ports, and Health Care Reform | 0.874 | 5 | 2 | 100% |
| 6 | Angrist, Joshua D., Imbens, Guido W., Rubin, Donald B (1996) Identification of Causal Effects Using Instrumental Variables | 0.811 | 4 | 2 | 100% |
| 7 | Rücker, Gerta (1989) A Two-Stage Trial Design for Testing Treatment, Self-Selection and Treatment Preference Effects | 0.811 | 4 | 2 | 100% |
| 8 | Ida, Takanori, Ishihara, Takunori, Ito, Koichiro, Kido, Daido, Kitag… (2022) Choosing Who Chooses: Selection-Driven Targeting in Energy Rebate Programs self | 0.737 | 3 | 2 | 100% |
| 9 | Long, Qi, Little, Roderick J., Lin, Xihong (2008) Causal Inference in Hybrid Intervention Trials Involving Treatment Choice | 0.737 | 3 | 2 | 100% |
| 10 | Athey, Susan, Wager, Stefan (2021) Policy Learning With Observational Data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.