Explore causal inference in observational studies through a 32-minute conference talk by Emma McCoy, Professor of Statistics at Imperial College London. Delve into time-series and causal inference methodology for robust estimation of treatment effects, particularly in transport settings. Learn about confounding, Directed Acyclic Graphs (DAGs), and the Potential Outcomes Framework. Discover alternative methods and gain insights from Emma's extensive experience in Mathematics and Statistics education. Understand the challenges of monitoring complex systems and the dynamics of data science. This talk, part of the Stanford Women in Data Science conference hosted by the Alan Turing Institute, offers a unique opportunity to engage with cutting-edge research and connect with leaders in the field of data science.
Causal Inference in Observational Studies - Emma McCoy, Imperial College London
Alan Turing Institute via YouTube
Overview
Syllabus
Introduction
Emmas background
Data analysis
Other datasets
confounding
DAG
Potential Outcomes Framework
Example
Ronald Fisher
Alternative methods
Taught by
Alan Turing Institute
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