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Explore regression-based techniques for computing causal effects in this 19-minute video, part of a series on causal effects. Learn about linear regression, double machine learning, and metalearners including T-learner, S-learner, and X-learner. Gain insights into what regression is and how it can be applied to causal inference. Follow along with Python code examples and access additional resources on causal inference and machine learning. Suitable for those interested in data science, statistics, and causal analysis.
Syllabus
Intro -
What is regression? -
3 Regression-based Techniques -
1 Linear Regression -
2 Double Machine Learning -
3 Metalearners -
3.1 T-learner -
3.2 S-learner -
3.3 X-learner -
Example Code -
Taught by
Shaw Talebi