Causal Discovery Insights
The discussion highlights the importance of informative variable names in causal models, particularly in the context of neuropathic pain data. Models are showing improved accuracy in pairwise and full graph discovery, yet concerns remain about the reliability of the directed acyclic graphs (DAGs) used for downstream analysis. Practitioners express anxiety over potentially incorrect models leading to flawed conclusions, emphasizing the need for careful specification in causal inference tasks.In this clip
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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Are LLMs Good at Causal Reasoning? with Robert Osazuwa Ness - 638
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