Title: Superoptimal regimes for decision-making assisted by algorithms
Authors: Mats Stensrud - Ecole polytechnique federale de Lausanne (Switzerland) [presenting]
Abstract: Healthcare providers 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 treatment decisions, adapted to a patient's characteristics. We will introduce superoptimal regimes, which are guaranteed to outperform conventional optimal regimes. Importantly, identification of superoptimal regimes and their values require exactly the same assumptions as identification of conventional optimal regimes in several common settings, including instrumental variable settings. The superoptimal regimes can also be identified in data fusion contexts, in which experimental data and (possibly confounded) observational data are available. We will present two examples that have appeared in the optimal regimes literature, illustrating that the superoptimal regimes perform better than conventional optimal regimes.