This is the 5th video in a series on causal effects. In the previous videos, we discussed different ways to compute treatment effects from data.

Series Playlist:    • Causality  

More on Causal Effects:
- Blog:   / causal-effects-via-regression  

More resources:
[1] Causal inference using regression on the treatment variable by Andrew Gelman and Jennifer Hill - http://www.stat.columbia.edu/~gelman/...
[2] Double/Debiased Machine Learning for Treatment and Causal Parameters by Victor Chernozhukov et al. - https://arxiv.org/abs/1608.00060
[3] DoubleML Python library: https://docs.doubleml.org/stable/guid...
[4] Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning by Kunzel et al. - https://arxiv.org/abs/1706.03461
[Data] Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. (CC BY 4.0)

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Intro - 0:00
What is regression? - 0:25
3 Regression-based Techniques - 2:26
1) Linear Regression - 2:47
2) Double Machine Learning - 5:26
3) Metalearners - 9:02
3.1) T-learner - 9:29
3.2) S-learner - 11:24
3.3) X-learner - 12:56
Example Code - 15:12