| Abstract |
Policy makers desire to implement decision rules that, when applied to individuals in the population of interest, yield the best possible outcomes. For example, the current focus on precision medicine reflects the search for individualized decision rules, adapted to a patient's characteristics. In this presentation, I will study how to define, choose, and estimate effects that inform individualized decisions. A central difficulty, common to most existing approaches, is that as we include more covariates and aim for finer personalization, the required assumptions become stronger.
As an alternative, I propose a strategy for detecting and estimating group-level effects, with statistical guarantees that the estimated groups truly differ. I then show that, in realistic settings, group-based decision rules can substantially outperform state-of-the-art optimal-regime methods, even when those methods rely on correctly specified models and are implemented with modern doubly robust machine-learning estimators. |
