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AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - #616
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Causal Discovery in Machine Learning: Examining Equilibrium Data and Supervised Learning Methods
In this case, it was equilibrium data. So we're assuming that once the intervention has been applied, you see the equilibrium consequences of the intervention. And is this necessarily a scenario that involves time series data where you're able to look at propagation or are they a data set of independent interventions and outcomes. Okay. Super interesting. Was there another paper that you wanted to touch on in this topic? Yeah, this is another paper by called learning to induce causal structure. Some of the authors overlap with the previous papers.
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