This is the first video in a series on causal effects. Here I introduce the Potential Outcomes Framework and use it to formulate 3 different types of causal effects. In future videos, I discuss how to compute causal effects from observational data.

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

Resources:
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter C. Austin
Counterfactuals and Causal Inference: Methods and Principles for Social Research by Stephen L. Morgan & Christopher Winship
An Introduction to Causal Inference by Judea Pearl

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Homepage: https://shawhintalebi.com/

Introduction - 0:00
Causal Effects - 0:24
3 Types of Variables - 1:01
Potential Outcomes Framework - 1:50
3 Types of Causal Effects - 2:28
1) Individual Treatment Effect (ITE) - 2:38
2) Average Treatment Effect (ATE) - 4:01
2.1) ATE in RCTs - 5:24
3) Average Treatment Effect of Treated/Controls (ATT/ATC) - 6:56
Practical Questions - 9:07