Theory and Methods Challenges in Counterfactual Prediction - Karla Diaz-Ordaz
Alan Turing Institute via YouTube
Overview
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Explore the intersection of AI prediction algorithms and causal inference in this 24-minute workshop video from the Alan Turing Institute. Delve into the limitations of traditional prediction methods and discover how causal inference can enhance decision-making capabilities. Learn about counterfactual prediction and its applications in healthcare and fairness assessments. Examine methodological challenges and potential solutions presented by a team of twelve academics specializing in predictive modeling, machine learning, and causal inference. Gain insights into decision support tools for scenarios like the COVID-19 pandemic. Cover topics including decision-making tools, causal assumptions, positivity violations, overlap issues, and real-world examples such as pneumonia treatment decisions.
Syllabus
Intro
Exercise
Decisionmaking tools
Causal assumptions
Lack of positivity
Random positivity violations
Poor overlap
Linear vs quadratic
Pneumonia example
Two more challenges
Conclusion
Taught by
Alan Turing Institute