Seminar Series: Dr. Robert Platt

Machine Learning for Confounding Control in Pharmacoepidemiology

Dr. Robert Platt

Departments of Epidemiology and Biostatistics, and Occupational Health and of Pediatrics
Albert Boehringer I Chair in Pharmacoepidemiology
McGill University

Short Biography:
Robert Platt is Professor in the Departments of Epidemiology, Biostatistics, and Occupational Health, and of Pediatrics, at McGill University. He holds the Albert Boehringer I endowed chair in Pharmacoepidemiology. Dr. Platt is principal investigator of the Canadian Network for Observational Drug Effect Studies (CNODES). His research focuses on improving methods for the study of medications using administrative data, with an emphasis on methods for causal inference and a substantive focus on medications in pregnancy. Dr. Platt is an editor-in-chief of Statistics in Medicine and is on the editorial boards of the American Journal of Epidemiology and Pharmacoepidemiology and Drug Safety. He has published over 400 articles, one book and several book chapters on biostatistics and epidemiology.

Machine learning tools are used extensively for prediction, but they are not typically designed for causal inference. However, tools such as targeted learning have been developed to exploit the strengths of machine learning in causal inference. In this presentation I will describe machine learning and its use to infer causation in pharmacoepidemiology. I will discuss settings in which machine learning can be used together with appropriate methods for confounding control, when it is useful, and when it may be unnecessary.

pharmacoepidemiology; machine learning; causal inference

Date: Friday, February 10th
Time: 1:30 pm - 2:30 pm
Location: PHFM 3015 (Western Centre for Public Health and Family Medicine)