Ehsan Karim, University of BC
Tuesday, Feb. 23, 2021
12-1 pm PST
Health authorities routinely collect patient information on a massive scale, but health researchers face the challenge of exploring cause-and-effect relationships using these non-randomized population-based data sources. Machine learning or AI methods are increasingly used to analyze large datasets. However, they may not inherently consider causal structures while modelling and may lead to less-than-optimal or even erroneous causal conclusions. We will discuss a statistical framework that attempts to accommodate these machine learning methods with appropriate epidemiologic principles in mind.
Dr. M. Ehsan Karim is an Assistant Professor at the UBC School of Population and Public Health, a Scientist at the Centre for Health Evaluation and Outcome Sciences (CHÉOS), and a Michael Smith Foundation for Health Research (MSFHR) Scholar. He obtained his PhD in Statistics from UBC. He completed his postgraduate training in the Department of Epidemiology, Biostatistics, and Occupational Health at McGill University, and was also a trainee at the Canadian Network for Observational Drug Effect Studies (CNODES). His current research focuses on causal inference and real-world observational data analyses, in both cross-sectional and longitudinal settings; applications of machine learning approaches in the context of electronic healthcare databases; patient-oriented research and survey sampling methodologies in epidemiologic studies.
This seminar will be conducted via Zoom. Please contact Joyce (firstname.lastname@example.org) for the link.