arXiv 31 Jul 2026 · Econometrics
arXiv:2607.29281 · PDF · Extracted main text
Applied research in economics is intrinsically motivated by broad normative objectives. However, it is not obvious how a researcher should direct their efforts to produce evidence toward such objectives. This paper reviews recent theoretical developments on research design for policy choice and provides new tools applied researchers can use to guide their design choices and communicate their policy recommendations. First, I focus on theoretical contributions in econometrics and provide a general framework that nests all the contexts and results reviewed using a coherent notation and narrative. Then, I present two diagrams applied researchers can use to navigate the theoretical literature starting from concrete scenarios to make thoughtful design choices. Finally, I introduce a new R package that produces one table and two figures applied researchers can plug in their `policy implications' section to provide evidence on the performance of different policy recommendations coming out of their study. The use of such tools is illustrated with an example in development economics.
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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 Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice | 1.000 | 25 | 3 | 100% |
| 2 | Manski, Charles F Statistical Treatment Rules for Heterogeneous Populations | 1.000 | 24 | 5 | 100% |
| 3 | Hirano, Keisuke and Porter, Jack R Asymptotics for Statistical Treatment Rules | 1.000 | 9 | 3 | 100% |
| 4 | Stoye, Jörg Minimax regret treatment choice with covariates or with limited validity of experiments | 1.000 | 8 | 3 | 100% |
| 5 | L. J. Savage (1951) The Theory of Statistical Decision | 0.928 | 4 | 4 | 100% |
| 6 | Athey, Susan and Wager, Stefan Policy Learning With Observational Data | 0.874 | 12 | 2 | 100% |
| 7 | Mbakop, Eric and Tabord-Meehan, Max Model Selection for Treatment Choice: Penalized Welfare Maximization | 0.874 | 8 | 2 | 100% |
| 8 | Hussam, Reshmaan and Rigol, Natalia and Roth, Benjamin N Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field | 0.874 | 7 | 2 | 100% |
| 9 | Christopher Adjaho and Timothy Christensen (2025) Externally Valid Policy Choice | 0.874 | 6 | 2 | 100% |
| 10 | Kohei Yata (2025) Optimal Decision Rules Under Partial Identification | 0.874 | 6 | 2 | 100% |
Showing the top 10 of 53 scored citations.