Super Data Science: ML & AI Podcast with Jon Krohn cover image

607: Inferring Causality

Super Data Science: ML & AI Podcast with Jon Krohn

CHAPTER

Bayesian Statistics and Causal Inference

Exploring the significance of Bayesian statistics in inferring algorithm performance and causal inferences, focusing on concepts like priors, posteriors, and parameter uncertainties. Introducing tools like BART and Think Causal to assist users in simplifying the process of utilizing new methods and running causal models on their data.

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