Causal Effects via the Do-operator | Overview & Example

This is the 3rd video in a series on causal effects. Here I discuss a new way to formulate the average treatment effect (ATE) using the do-operator. This alternative formulation unlocks new paths toward estimating causal effects from observational data.

Series Playlist:    • Causality  
📰 Read more: https://towardsdatascience.com/causal...

Resources:
- An Introduction to Causal Inference by Judea Pearl: https://www.degruyter.com/document/do...
- An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter Austin: https://www.tandfonline.com/doi/full/...

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Introduction - 0:00
Observational vs Interventional Data - 0:35
2 Formulations of ATE - 2:23
do-operator - 5:26
Identifiability - 7:05
Truncated Factorization Formula - 10:34
Coping with Unmeasured Confounders - 10:52
Interventional Distribution via Parents - 12:34
Key Points - 13:08