Answering causal questions is a strong focus within population health and health care research. The recent prominence of machine learning methods and emphasis on prediction research has reinforced debates about the important conceptual differences between causal and prediction research. However, more recently, the conversations have highlighted the potential synergies in how machine learning methods can be embedded within a causal framework to improve causal effect estimation. This event will feature a keynote speaker and panellists from biostatistics, machine learning, population health and healthcare backgrounds to unpack the intersection of prediction, machine learning and casual inference and what this means for the disciplines embedded within Schools of Public Health.
Professor Tony Blakely , University of Melbourne School of Population and Global Health, will deliver a keynote discussing his latest ideas related to machine learning and causal inference building on his International Journal of Epidemiology paper, “Reflection on modern methods: when worlds collide – prediction, machine learning and causal inference”
Michael Chaiton, Scientist, Centre for Addiction and Mental Health
Olli Saarela, Associate Professor, Dalla Lana School of Public Health, University of Toronto
Vishwali Mhasawade, Phd Candidate, Computer Science and Engineering, Chunara Lab, New York University
Elham Dolatabadi, Assistant Professor, IHPME, Dalla Lana School of Public Health, University of Toronto and Staff Scientist at Vector Institute
Laura Rosella, Associate Professor, Dalla Lana School of Public Health, University of Toronto and Scientific Director of the Population Health Analytics Lab
This event is a co-presentation of the Data Science Interdisciplinary Research Cluster at the University of Toronto’s Dalla Lana School of Public Health, and the Centre for Addiction and Mental Health (Toronto, Canada)