909: Causal AI, with Dr. Robert Usazuwa Ness
Super Data Science: ML & AI Podcast with Jon Krohn
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The Do Operator in Causal AI
This chapter explores the functionality of the do operator in Bayesian modeling, focusing on simulating causal relationships in the absence of real-world experiments. It discusses the distinction between causal AI and Bayesian statistics, emphasizing the importance of causal assumptions and how they can be applied in practical scenarios, such as determining the effects of interventions. The conversation highlights the complexities of causal inference, the role of biases, and the necessity of careful data collection to achieve reliable outcomes.
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