Causal Effects via the Do-operator: Overview and Example - Part 3

Explore the concept of causal effects through the do-operator in this 15-minute video, the third installment of a series on causal effects. Delve into a new approach for formulating the average treatment effect (ATE) using the do-operator, unlocking novel methods for estimating causal effects from observational data. Learn about the differences between observational and interventional data, examine two formulations of ATE, and understand the do-operator's role in causal inference. Investigate the concept of identifiability, the truncated factorization formula, and strategies for dealing with unmeasured confounders. Discover how to derive interventional distributions via parents and grasp key points that tie the concepts together. Access additional resources, including academic papers on causal inference and propensity score methods, to deepen your understanding of this complex topic.

Introduction -
Observational vs Interventional Data -
2 Formulations of ATE -
do-operator -
Identifiability -
Truncated Factorization Formula -
Coping with Unmeasured Confounders -
Interventional Distribution via Parents -
Key Points -