Social Science Statistics Blog

The paper uses Bayes Rule to conceptualize the decision a doctor has to make when hearing symptom reports from a patient and has to decide whether the patient really has the disease:

Pr(Disease | Symptom) = Pr(Symptom | Disease) * Pr(Disease) / Pr(Symptom)

A doc would decide differently if she believed that disease prevalence differs across racial groups (which affects Pr(Disease)), or if diagnostic signals are more noisy from some groups (which changes Pr(symptom)), maybe because the quality of doctor-patient communication differs across races.

The authors test their model on diagnosis data from family physicians and internists, and find that sensible priors about disease prevalance could explain racial differences in the diagnosis of hypertension and diabetes. For the diagnosis of depression there is evidence that differences in doctors' decisions may be driven by different communication patterns between white docs and their white vs. minority patients.

Obviously prejudice and stereotyping are different from statistical discriminiation, and have quite different policy implicatons. This is a really nice paper that makes these distinctions clear as well as nicely using Bayes Rule to conceptualize the issues. The general idea might also apply to other issues of policy including police stop and search.