Causal Effects via DAGs: Handling Unobserved Confounders - Part 4

Explore the fourth video in a series on causal effects, focusing on handling models that are not Markovian. Delve into two graphical criteria for evaluating causal effects in the presence of unobserved confounders. Learn about identifiability, Markovian models, back door paths, blocking, and the back door and front door criteria. Gain insights from resources like Judea Pearl's introduction to causal inference and Tian & Shiptser's work on identifying causal effects. Follow along with the video's structure, covering topics from the introduction to the detailed explanation of front door criterion in just 14 minutes.