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12min chapter
613: Causal Machine Learning
Super Data Science: ML & AI Podcast with Jon Krohn
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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