Working Paper: NBER ID: w29358
Authors: Charles F. Manski
Abstract: 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.
Keywords: No keywords provided
JEL Codes: C44; I19
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| predicted probability of illness (p(y=1|x)) (C25) | surveillance is the preferred treatment option (E63) |
| predicted probability of illness (p(y=1|x)) (C25) | aggressive treatment is favored (E63) |