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#66 ALEXANDER MATTICK - [Unplugged / Community Edition]

Machine Learning Street Talk (MLST)

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Navigating Causality in Machine Learning

This chapter explores the complexities of causal representation learning, emphasizing the significance of understanding causal structures in cognitive categories and challenges posed by observational data. It also addresses philosophical debates around causality, program synthesis, and the implications for machine learning practices, illustrated through real-world examples like Zillow's predictive failures.

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