arXiv 2 Oct 2021 · Econometrics · publishedJournal of Econometrics (2022) · 2 citations (OpenAlex)
arXiv:2110.00864 · PDF · DOI · OpenAlex
This paper extends my research applying statistical decision theory to treatment choice with sample data, using maximum regret to evaluate the performance of treatment rules. The specific new contribution is to study as-if optimization using estimates of illness probabilities in clinical choice between surveillance and aggressive treatment. Beyond its specifics, the paper sends a broad message. Statisticians and computer scientists have addressed conditional prediction for decision making in indirect ways, the former applying classical statistical theory and the latter measuring prediction accuracy in test samples. Neither approach is satisfactory. Statistical decision theory provides a coherent, generally applicable methodology.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Optimal Decision Rules when Payoffs are Partially Identified | 1.000 | 5 | 3 |
| 2 | Inference for Interval-Identified Parameters Selected from an Estimated Set | 0.405 | 1 | 1 |
| 3 | Designing Algorithmic Recommendations to Achieve Human–AI Complementarity | 0.405 | 1 | 1 |
| 4 | Policy Learning with Confidence$^$ | 0.405 | 1 | 1 |