Explore the fundamentals of causal effects in this introductory video from a series on the topic. Delve into the Potential Outcomes Framework and learn about three distinct types of causal effects. Discover how to formulate these effects and gain insights into their applications in observational data analysis. Cover key concepts including the three types of variables, Individual Treatment Effect (ITE), Average Treatment Effect (ATE), and Average Treatment Effect of Treated/Controls (ATT/ATC). Examine practical questions and real-world applications of these concepts. Prepare for future videos in the series that will expand on computing causal effects from observational data. Access additional resources and readings to deepen your understanding of causal inference methods and principles in social research.
Overview
Syllabus
Introduction -
Causal Effects -
3 Types of Variables -
Potential Outcomes Framework -
3 Types of Causal Effects -
1 Individual Treatment Effect ITE -
2 Average Treatment Effect ATE -
2.1 ATE in RCTs -
3 Average Treatment Effect of Treated/Controls ATT/ATC -
Practical Questions -
Taught by
Shaw Talebi
Related Courses
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Potential Outcomes Framework for Causal Inference
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A Crash Course in Causality: Inferring Causal Effects from Observational Data
4.0 -
Causal Effects via the Do-operator: Overview and Example - Part 3
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Causal Effects via Propensity Scores - Introduction and Python Implementation
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Causal Inference in Observational Studies - Emma McCoy, Imperial College London
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Statistical Limits of Causal Inference
