arXiv 17 Apr 2024 · Econometrics · publishedJournal of Econometrics (2025) · 2 citations (OpenAlex)
arXiv:2404.11767 · PDF · DOI · OpenAlex · Extracted main text
Threshold policies are decision rules that assign treatments based on whether an observable characteristic exceeds a certain threshold. They are widespread across multiple domains, including welfare programs, taxation, and clinical medicine. This paper examines the problem of designing threshold policies using experimental data, when the goal is to maximize the population welfare. First, I characterize the regret - a measure of policy optimality - of the Empirical Welfare Maximizer (EWM) policy, popular in the literature. Next, I introduce the Smoothed Welfare Maximizer (SWM) policy, which improves the EWM's regret convergence rate under an additional smoothness condition. The two policies are compared by studying how differently their regrets depend on the population distribution, and investigating their finite sample performances through Monte Carlo simulations. In many contexts, the SWM policy guarantees larger welfare than the EWM. An empirical illustration demonstrates how the treatment recommendations of the two policies may differ in practice.
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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 | 18 | 5 | 100% |
| 2 | Athey, S. and S. Wager (2021) Policy learning with observational data | 1.000 | 9 | 3 | 100% |
| 3 | Horowitz, J. L (1992) A smoothed maximum score estimator for the binary response model | 0.737 | 4 | 3 | 50% |
| 4 | Chernoff, H (1964) Estimation of the mode | 0.737 | 3 | 3 | 67% |
| 5 | Manski, C. F (1975) Maximum score estimation of the stochastic utility model of choice | 0.737 | 3 | 2 | 100% |
| 6 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.737 | 3 | 2 | 100% |
| 7 | Mbakop, E. and M. Tabord-Meehan (2021) Model selection for treatment choice: Penalized welfare maximization | 0.737 | 3 | 2 | 100% |
| 8 | Kim, J. and D. Pollard (1990) Cube root asymptotics | 0.669 | 10 | 3 | 30% |
| 9 | Banerjee, M. and I. W. McKeague (2007) Confidence sets for split points in decision trees | 0.644 | 2 | 2 | 100% |
| 10 | Card, D., C. Dobkin, and N. Maestas (2008) The impact of nearly universal insurance coverage on health care utilization: Evidence from medicare | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Semiparametric Efficiency in Policy Learning with General Treatments | 1.000 | 5 | 3 |
| 2 | Compound Selection Decisions: An Almost SURE Approach | 0.737 | 3 | 2 |
| 3 | 2606.01659 | 0.644 | 2 | 2 |
| 4 | Leave No One Undermined: Policy Targeting with Regret Aversion | 0.405 | 1 | 1 |
| 5 | Statistical Decisions and Partial Identification: With Application to Boundary Discontinuity Design | 0.405 | 1 | 1 |