arXiv 20 Dec 2019 · Econometrics · publishedJournal of the American Statistical Association (2023) · 16 citations (OpenAlex)
arXiv:1912.10014 · PDF · DOI · OpenAlex · Extracted main text
Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals. The optimal dynamic treatment regime is a regime that maximizes counterfactual welfare. We introduce a framework in which we can partially learn the optimal dynamic regime from observational data, relaxing the sequential randomization assumption commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes and establish mapping from data to partial ordering via a set of linear programs. We then characterize the identified set of the optimal regime as the set of maximal elements associated with the partial ordering. We relate the notion of partial ordering with a more conventional notion of partial identification using topological sorts. Practically, topological sorts can be served as a policy benchmark for a policymaker. We apply our method to understand returns to schooling and post-school training as a sequence of treatments by combining data from multiple sources. The framework of this paper can be used beyond the current context, e.g., in establishing rankings of multiple treatments or policies across different counterfactual scenarios.
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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 | Murphy, S. A., M. J. van der Laan, J. M. Robins, and C. P. P. R. Group (2001) Marginal mean models for dynamic regimes | 0.843 | 4 | 3 | 75% |
| 2 | Murphy, S. A (2003) Optimal dynamic treatment regimes | 0.843 | 5 | 3 | 60% |
| 3 | Kitagawa, T. and A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.843 | 3 | 3 | 100% |
| 4 | Cui, Y. and E. Tchetgen Tchetgen (2020) A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity | 0.737 | 3 | 2 | 100% |
| 5 | Han, S self | 0.737 | 3 | 2 | 100% |
| 6 | Conduct Problems Prevention Research Group (1992) A developmental and clinical model for the prevention of conduct disorder: The FAST Track Program | 0.644 | 2 | 2 | 100% |
| 7 | Deb, R., Y. Kitamura, J. K.-H. Quah, and J. Stoye (2017) Revealed price preference: Theory and stochastic testing | 0.644 | 2 | 2 | 100% |
| 8 | Imbens, G. W. and J. D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.644 | 2 | 2 | 100% |
| 9 | Johnson, R. C. and C. K. Jackson (2019) Reducing inequality through dynamic complementarity: Evidence from Head Start and public school spending | 0.644 | 2 | 2 | 100% |
| 10 | Machado, C., A. Shaikh, and E. Vytlacil (2019) Instrumental variables and the sign of the average treatment effect | 0.644 | 2 | 2 | 100% |
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