Abstract: Health care is moving toward analytic systems that take large databases and estimate varying quantities of interest both quickly and robustly, incorporating advances from statistics, econometrics, and computer science. The massive size of the health care sector make data science applications in this space particularly salient for social policy. This presentation will discuss specific challenges related to developing and deploying statistical machine learning algorithms for health economics and outcomes research. Considerations go beyond typical measures of statistical assessment, and include concepts such as dataset shift and algorithmic fairness. An overarching theme is that developing methodology tailored to specific substantive health problems and the associated electronic health data is critical given the stakes involved.
Biography: Sherri Rose, Ph.D. is an Associate Professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research. She is also Co-Director of the Health Policy Data Science Lab. Her honors include an NIH Director's New Innovator Award, the ISPOR Bernie J. O'Brien New Investigator Award, Mid-Career Awards from the American Statistical Association and Penn-Rutgers Center for Causal Inference, and Fellow designation from the American Statistical Association.