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  

Read More:   / causaleffectsviaregression  
Example Code: https://github.com/ShawhinT/YouTubeB...

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] Metalearners 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 Regressionbased Techniques 2:26
1) Linear Regression 2:47
2) Double Machine Learning 5:26
3) Metalearners 9:02
3.1) Tlearner 9:29
3.2) Slearner 11:24
3.3) Xlearner 12:56
Example Code 15:12