arXiv 21 Nov 2021 · Econometrics
arXiv:2111.10904 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the problem of estimating individualized treatment rules when treatment effects are partially identified, as it is often the case with observational data. By drawing connections between the treatment assignment problem and classical decision theory, we characterize several notions of optimal treatment policies in the presence of partial identification. Our unified framework allows to incorporate user-defined constraints on the set of allowable policies, such as restrictions for transparency or interpretability, while also ensuring computational feasibility. We show how partial identification leads to a new policy learning problem where the objective function is directionally -- but not fully -- differentiable with respect to the nuisance first-stage. We then propose an estimation procedure that ensures Neyman-orthogonality with respect to the nuisance components and we provide statistical guarantees that depend on the amount of concentration around the points of non-differentiability in the data-generating-process. The proposed methods are illustrated using data from the Job Partnership Training Act study.
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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 A. Tetenov (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 1.000 | 15 | 6 | 100% |
| 2 | Chernozhukov, V., J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally robust semiparametric estimation | 1.000 | 9 | 3 | 100% |
| 3 | Kasy, M. (2016, 03) (2016) Partial Identification, Distributional Preferences, and the Welfare Ranking of Policies | 1.000 | 8 | 3 | 100% |
| 4 | Pu, H. and B. Zhang (2021, mar) (2021) Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach | 1.000 | 7 | 4 | 100% |
| 5 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 1.000 | 6 | 4 | 100% |
| 6 | Foster, D. J. and V. Syrgkanis (2019) Orthogonal statistical learning | 1.000 | 5 | 3 | 100% |
| 7 | Athey, S. and S. Wager (2021) Policy learning with observational data | 0.964 | 19 | 6 | 89% |
| 8 | Byambadalai, U (2022) Identification and inference for welfare gains without unconfoundedness | 0.874 | 6 | 2 | 100% |
| 9 | Christensen, T., H. R. Moon, and F. Schorfheide (2022) Optimal discrete decisions when payoffs are partially identified | 0.874 | 5 | 2 | 100% |
| 10 | Cui, Y. and E. T. Tchetgen (2021) A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity | 0.874 | 5 | 2 | 100% |
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