Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/217108
Authors: 
Manski, Charles F.
Year of Publication: 
2018
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 9 [Year:] 2018 [Issue:] 2 [Pages:] 541-569
Abstract: 
This paper studies an identification problem that arises when clinicians seek to personalize patient care by predicting health outcomes conditional on observed patient covariates. Let y be an outcome of interest and let (x=k, w=j) be observed patient covariates. Suppose a clinician wants to choose a care option that maximizes a patient's expected utility conditional on the observed covariates. To accomplish this, the clinician needs to know the conditional probability distribution P(y=k, w=j). It is common to have a trustworthy evidence-based risk assessment that predicts y conditional on a subset of the observed covariates, say x, but not conditional on (x, w). Then the clinician knows P(y=k) but not P(y=k, w=j). Research on the ecological inference problem studies partial identification of P(y, w) given knowledge of P(y) and P(w). Combining this knowledge with structural assumptions yields tighter conclusions. A psychological literature comparing actuarial predictions and clinical judgments has concluded that clinicians should not attempt to subjectively predict patient outcomes conditional on covariates that are not utilized in evidence-based risk assessments. I argue that formalizing clinical judgment through analysis of the identification problem can improve risk assessments and care decisions.
Subjects: 
Ambiguity
clinical judgment
partial identification
patient care
JEL: 
C14
I19
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/4.0/
Document Type: 
Article

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