Credible ecological inference for medical decisions with personalized risk assessment

Charles F. Manski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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|x=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|x=k) but not P(y|x=k, w=j). Research on the ecological inference problem studies partial identification of P(y|x, w) given knowledge of P(y|x) and P(w|x). 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.

Original languageEnglish (US)
Pages (from-to)541-569
Number of pages29
JournalQuantitative Economics
Issue number2
StatePublished - Jul 2018


  • Ambiguity
  • clinical judgment
  • partial identification
  • patient care

ASJC Scopus subject areas

  • Economics and Econometrics


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