Causal Effects via Propensity Scores | Introduction & Python Code

This is the 2nd video in a series on causal effects. Here I introduce the Propensity Score and discuss 3 ways we can use it to compute causal effects from observational data. At the end, I share a concrete example with code of what using these methods might look like in practice.

👉 Series Playlist:    • Causality  

📰 Read more: https://towardsdatascience.com/propen...
💻 Example Code: https://github.com/ShawhinT/YouTube-B...

Resources:
- An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies by Peter C. Austin
- Data from UCI MLR: https://archive.ics.uci.edu/ml/datase...

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Introduction - 0:00
Observational vs Interventional Studies - 0:32
Propensity Score - 3:25
3 Propensity Score-based Methods - 4:56
1) Matching - 5:18
2) Stratification - 9:07
3) Inverse Probability of Treatment Weighting - 10:37
Example: ATE of Grad on Income - 12:29
Word of Caution - 15:46