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Causal AI & Dynamical Systems || Naftali Weinberger || Causal Bandits Ep. 005 (2023)

Causal Bandits Podcast

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Navigating Assumptions in Causal Modeling

This chapter explores the critical role of assumptions in causal models, emphasizing that missing arrows signify strong assumptions rather than simplicity. It highlights the necessity of causal models for establishing relationships and making interventions, while critiquing common practices in fields like psychology. The discussion also examines the complexities of dynamic systems and interventions, illustrating how these factors influence predictive capabilities and the evaluation of causal inferences.

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