2021/10/02 by Charles F. Manski, Manski, Charles F.
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Statistical Methods and Inference #Statistical Methods in Clinical Trials #econ.EM #stat.ML
paper · pdf · doi:10.48550/arxiv.2110.00864
arxiv created 2021/10/02 · openalex publication_date 2021/10/02 · arxiv updated 2021/10/05 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.