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Causal AI, Modularity & Learning || Andrew Lawrence || Causal Bandits Ep. 002 (2023)

Causal Bandits Podcast

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Understanding Causality with Bayesian Non-Parametrics

This chapter explores the application of Bayesian non-parametric methods in modeling real-world data to uncover causality. It discusses the importance of accurately identifying causal relationships and the challenges of using generative AI within high-stakes domains. The conversation highlights different approaches for estimating causal effects and emphasizes the need for robust methods amidst evolving data landscapes.

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