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Causal Models, Biology, Generative AI & RL || Robert Ness || Causal Bandits Ep. 011 (2024)
Causal Bandits Podcast
Causal Modeling and Graphical Identification in Probabilistic Programming
Exploring the application of Bayesian approaches and causal identification using graphical models in probabilistic programming frameworks. Discussion on leveraging algorithms like Y0 and implementing Bayesian reasoning to estimate causal effects and uncertainties. Introducing libraries like Cairo or Hiro for counterfactual reasoning and addressing challenges in causal inference within machine learning frameworks.
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