613: Causal Machine Learning
Super Data Science: ML & AI Podcast with Jon Krohn
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Creation of DUI Library for Causal Inference in Machine Learning
The chapter delves into the development of the DUI library focused on teaching causal inference in machine learning, with a distinction between Pi Y and Do Y explained. It emphasizes the four key steps of causal inference: modeling assumptions, identification of causal effects, statistical estimation, and validation. The conversation explores the significance of causal machine learning in building more robust models for decision-making scenarios while highlighting the challenges and potential downsides of transitioning to causal methods.
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