Causal Inference in Statistics: Why, What, and How

May 10, 2023


Virtual


Description

 An introductory overview on the goals of causal inference, key quantities, and typical methods will be given for situations where an exposure of interest is set at a chosen baseline (“point exposure”) and the target outcome arises at a later time point, focusing on a binary outcome and continuous exposure. Using the potential outcomes framework, principled definitions of causal effects will be presented along with estimation approaches which invoke the no unmeasured confounding assumption.


Featured Speakers

Speaker: Erica E. M. Moodie
Speaker Erica E. M. Moodie
Erica E. M. Moodie is a Professor of Biostatistics at McGill University and a Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine. She obtained her MPhil in Epidemiology in 2001 from the University of Cambridge and a PhD in Biostatistics in 2006 from the University of Washington, before …

Erica E. M. Moodie is a Professor of Biostatistics at McGill University and a Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine. She obtained her MPhil in Epidemiology in 2001 from the University of Cambridge and a PhD in Biostatistics in 2006 from the University of Washington, before joining the faculty at McGill. Her main research interests are in causal inference and longitudinal data with a focus on precision medicine. She is the 2020 recipient of the CRM-SSC Prize in Statistics and an Elected Member of the International Statistical Institute. Dr Moodie serves as an Associate Editor of Biometrics and a Statistical Editor of Journal of Infectious Diseases. She holds a chercheur de merite career award from the Fonds de recherche du Quebec-Sante.

Full Description


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Date and Time

Wed, May 10, 2023


Location

Virtual