Mats Stensrud: “On optimal treatment regimes assisted by algorithms“

Abstract
Decision 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 introduce superoptimal decision rules, which are guaranteed to outperform conventional optimal decision rules. Importantly, identification of superoptimal rules and their values require exactly the same assumptions as identification of conventional optimal rules in several common settings, including common instrumental variable settings. The superoptimal rules can also be identified in data fusion contexts, in which experimental data and (possibly confounded) observational data are available. We study superoptimal rules in two examples that have been presented in the optimal decision rule literature, illustrating that the superoptimal rules perform better than conventional optimal rules.
Bio
Mats J. Stensrud, MD Dr philos, is a tenure-track assistant professor of biostatistics in the Department of Mathematics at EPFL in Switzerland. His research focuses on methods for causal inference in medicine and epidemiology, usually in settings with exposures and outcomes that depend on time. Before coming to EPFL, Dr. Stensrud was a Kolokotrones research fellow and Fulbright Research Scholar at Harvard School of Public Health.
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