This masterclass will focus on evidence-based decision making for patient care under uncertainty, in which clinicians face only limited ability to predict patients’ future illness and treatment response.

To deal with this inherent uncertainty, partial identification analysis can be applied to make credible predictions for patient outcomes. This analysis motivates the use of decision criteria with well understood properties.

Particular focus will be given to the minimax-regret criteria, which specifies a decision rule as uniformly close to the optimal decision rule as possible given the underlying uncertainty of patient outcomes.