Causal Effects via DAGs | How to Handle Unobserved Confounders
Key Takeaways
Handles unobserved confounders using causal DAGs
Original Description
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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: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa
Blog: https://medium.com/towards-data-science/causal-effects-via-dags-801df31da794?sk=aa0947ca29e23fb3c1612e40deac38cf
Resources:
- An Introduction to Causal Inference by Judea Pearl: https://www.degruyter.com/document/doi/10.2202/1557-4679.1203/html
- On Identifying Causal Effects by Tian & Shiptser: https://faculty.sites.iastate.edu/jtian/files/inline-files/tian-shpitser-2009.pdf
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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