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Introduction to Causal Inference and Directed Acyclic Graphs
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This presentation discusses causal inference and directed acyclic graphs. Viewers will learn the difference between description, prediction, and causal inference as three distinct scientific tasks requiring distinct scientific methods. Additionally, viewers will understand the main features of causal directed acyclic graphs and how they can be used to plan and interpret causal analysis and appreciate some of the challenges and implications of using directed acyclic graphs in applied research.
The presentation is structured as follows:
Part 1: Introduction to casual inference and directed acyclic graphs (40 minutes with 20-minute Q & A)
Part 2: Directed acyclic graphs in practice (40 minutes with 20-minute Q&A)
About the Speaker:
Dr. Peter WG Tennant is an Epidemiologist and Data Scientist with a primary interest in adapting and translating contemporary causal inference methods into the health and social sciences. He is Associate Professor of Health Data Science at the University of Leeds in the United Kingdom.
Categories: Guest Speaker, Research Data
The presentation is structured as follows:
Part 1: Introduction to casual inference and directed acyclic graphs (40 minutes with 20-minute Q & A)
Part 2: Directed acyclic graphs in practice (40 minutes with 20-minute Q&A)
About the Speaker:
Dr. Peter WG Tennant is an Epidemiologist and Data Scientist with a primary interest in adapting and translating contemporary causal inference methods into the health and social sciences. He is Associate Professor of Health Data Science at the University of Leeds in the United Kingdom.
Categories: Guest Speaker, Research Data
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