arXiv 15 May 2026 · Statistics — Applications
arXiv:2605.16593 · PDF · DOI · OpenAlex · Extracted main text
Decision-makers frequently must choose a single action from a finite set of alternatives -- for example, physicians selecting a treatment, investors choosing a portfolio risk level, or judges determining sentences. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. In this paper, I show how to generally derive policy rules from observational data in a multi-action framework under relatively weak assumptions about the underlying structure of the heterogeneous sampled population. Conditional average treatment effects (CATEs) are consistently estimated via a weighted K-means algorithm, assuming the outcome model is correctly specified within each homogeneous subgroup. Feasible policy rules are then implemented via a standard decision tree, allowing for both perfect and imperfect adherence to treatment. The methodology is applied to treatment options for Hepatitis C (HCV) among patients co-infected with human immunodeficiency virus (HIV), a setting in which no uniform guideline exists for modern pharmaceutical therapies. The results identify a subgroup of patients with approximately an 80% probability of spontaneous HCV clearance without treatment. Estimation results also show that reallocating treatments among treated individuals could have reduced total treatment costs by CAN$3.6-4.9 million while still increasing aggregate health benefits relative to the status quo. These findings demonstrate that the proposed approach can generate improved, data-driven treatment guidelines for the management of HIV/HCV co-infected patients.
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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 | Athey, Susan and Wager, Stefan (2021) Policy Learning With Observational Data | 1.000 | 8 | 3 | 100% |
| 2 | Wilton, James and Wong, Stanley and Yu, Amanda and Ramji, Alnoor and… (2020) Real-world Effectiveness of Sofosbuvir/Velpatasvir for Treatment of Chronic Hepatitis C in British Columbia, Canada: A Populatio… | 1.000 | 6 | 3 | 100% |
| 3 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.950 | 7 | 4 | 86% |
| 4 | Zhou, Zhengyuan and Athey, Susan and Wager, Stefan (2023) Offline Multi-Action Policy Learning: Generalization and Optimization | 0.874 | 7 | 2 | 100% |
| 5 | Manski, Charles F (2021) Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald | 0.811 | 4 | 2 | 100% |
| 6 | Saeed, Sahar and Thomas, Tyler and Dinh, Duy A and Moodie, Erica and… (2024) Frequent Disengagement and Subsequent Mortality Among People With HIV and Hepatitis C in Canada: A Prospective Cohort Study | 0.811 | 4 | 2 | 100% |
| 7 | Young, Jim and Wang, Shouao and Lanièce Delaunay, Charlotte and Coop… (2023) The rate of hepatitis C reinfection in Canadians coinfected with HIV and its implications for national elimination | 0.811 | 4 | 2 | 100% |
| 8 | Langevin, Raphaël (2026) Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data self | 0.737 | 5 | 2 | 60% |
| 9 | AASLD (2022) Patients With HIV/HCV Coinfection | 0.737 | 3 | 2 | 100% |
| 10 | Chernozhukov, Victor and Demirer, Mert and Duflo, Esther and Fernand… (2025) Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an… | 0.737 | 3 | 2 | 100% |
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