arXiv 20 Jun 2022 · Econometrics
arXiv:2206.09883 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the identification and estimation of individualized intervention policies in observational data settings characterized by endogenous treatment selection and the availability of instrumental variables. We introduce encouragement rules that manipulate an instrument. Incorporating the marginal treatment effects (MTE) as policy invariant structural parameters, we establish the identification of the social welfare criterion for the optimal encouragement rule. Focusing on binary encouragement rules, we propose to estimate the optimal policy via the Empirical Welfare Maximization (EWM) method and derive convergence rates of the regret (welfare loss). We consider extensions to accommodate multiple instruments and budget constraints. Using data from the Indonesian Family Life Survey, we apply the EWM encouragement rule to advise on the optimal tuition subsidy assignment. Our framework offers interpretability regarding why a certain subpopulation is targeted.
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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, Toru and Tetenov, Aleksey (2018) Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 0.941 | 6 | 5 | 83% |
| 2 | Yu-Chang Chen and Haitian Xie (2022) Personalized Subsidy Rules | 0.928 | 4 | 3 | 100% |
| 3 | Sasaki, Yuya and Ura, Takuya (2024) Welfare Analysis via Marginal Treatment Effects | 0.874 | 6 | 2 | 100% |
| 4 | James J. Heckman and Edward Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.811 | 4 | 2 | 100% |
| 5 | Liyang Sun (2025) Empirical Welfare Maximization with Constraints | 0.811 | 4 | 2 | 100% |
| 6 | Athey, Susan and Wager, Stefan (2021) Policy Learning With Observational Data | 0.763 | 9 | 3 | 44% |
| 7 | Carneiro, Pedro and Lokshin, Michael and Umapathi, Nithin (2017) Average and Marginal Returns to Upper Secondary Schooling in Indonesia | 0.644 | 4 | 1 | 100% |
| 8 | Hongxiang Qiu and Marco Carone and Ekaterina Sadikova and Maria Petu… (2021) Optimal Individualized Decision Rules Using Instrumental Variable Methods | 0.644 | 3 | 2 | 67% |
| 9 | Brinch, Christian N. and Mogstad, Magne and Wiswall, Matthew (2017) Beyond LATE with a Discrete Instrument | 0.644 | 2 | 2 | 100% |
| 10 | Carneiro, Pedro and Heckman, James J. and Vytlacil, Edward (2010) Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Learning with New Treatments | 0.405 | 1 | 1 |
| 2 | Stochastic treatment choice with empirical welfare updating | 0.405 | 1 | 1 |
| 3 | Uniform Confidence Band for Marginal Treatment Effect Function | 0.405 | 1 | 1 |
| 4 | Policy Learning under Unobserved Confounding: A Robust and Efficient Approach | 0.405 | 1 | 1 |