Today’s clip is from episode 137 of the podcast, with Robert Ness.
Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling.
The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.
Get the full conversation here.
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Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
