Causal Effects | An introduction

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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.

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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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
  1. Shaw Talebi
  2. causality
  3. causal inference
  4. causal discovery
  5. machine learning
  6. data science
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Комментарии
Автор

Correction @5:26 - I meant to say "average treatment effect" 😅

ShawhinTalebi
Автор

Thank I was really confused before I discover your channel by sudden.You really are helping me prevent failing:)) Merci

yasamanroudaki
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This is a one million times better explanation than my professor. Thank you sir!

juandavidmunoz
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Thank you so much for your great explanations

mahdidehshiri
Автор

Hi Shawhin, thanks for the valuable information in the video. I have one question.
What if we have more than 1 treatment effect in the post-period?
Let's think about a campaign & sales scenario. We were using 3 campaigns and then we launched 2 more campaigns at a certain time (became totally 5). In the case of 1 newly launched campaign, I was planning to use a causal model to learn its effect but 2 campaigns together will create noise. How can I distinguish their effect from each other ? Do you know any alternative method for it?

mehmetkazanc