Causal Inference in Clinical Research: From Theory to Practice

Linbo Wang

Abstract: Causal inference is an important topic in healthcare because a causal relationship between an exposure and a health outcome may suggest an intervention to improve the health outcome. In this tutorial, we provide an introduction to the field of causal inference. We will cover several fundamental topics in causal inference, including the potential outcome framework, structural equation modeling, propensity score modeling, and instrumental variable analysis. Methods will be illustrated using real clinical examples.

Bio: Linbo Wang is an assistant professor in the Department of Statistical Sciences, University of Toronto. He is also an Affiliate Assistant Professor in the Department of Statistics, University of Washington, and a faculty affiliate at Vector Institute. His research interest is centered around causality and its interaction with statistics and machine learning. Prior to these roles, he was a postdoc at Harvard T.H. Chan School of Public Health. He obtained his Ph.D. from the University of Washington.

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