Causal Effects via DAGs | How to Handle Unobserved Confounders
🤝 Want your team maximizing Claude? I run 1:1 and team AI workshops for companies doing $1M+ per year: https://aibuilder.academy/yt/ASU5HG5EqTM This is the 4th video in a series on causal effects. In the last video, we saw that we could evaluate any causal effect for a Markovian causal model. However, the question remained of how to handle models that are not Markovian. In this video, we start to answer this question via two quick-and-easy graphical criteria for evaluating causal effects. Series Playlist: • Causality Blog: https://medium.com/towards-data-scien... Resources: An Introduction to Causal Inference by Judea Pearl: https://www.degruyter.com/document/do... On Identifying Causal Effects by Tian & Shiptser: https://faculty.sites.iastate.edu/jti... Introduction - 0:00 Identifiability - 0:28 Markovian Models - 2:12 Unobserved Confounders - 3:19 Back & Front Door Criteria - 4:18 Back Door Path - 4:44 Blocking - 5:22 Back Door Criterion - 7:27 Front Door Criterion - 9:14
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Judea Pearl: Do(x) Operator and Do-Calculus | AI Podcast Clips
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Kun Zhang: Methodological advances in causal representation learning
