A light hearted yet rigorous approach to learning impact estimation and sensitivity analysis. Everything in Python and with as many memes as I could find.

I like to think of this entire series as a tribute to Joshua Angrist, Alberto Abadie and Christopher Walters for their amazing Econometrics class. Most of the ideas here are taken from their classes at the American Economic Association. Watching them is what is keeping me sane during this tough year of 2020.

• Cross-Section Econometrics
• Mastering Mostly Harmless Econometrics

I'll also like to reference the amazing books from Angrist. They have shown me that Econometrics, or 'Metrics as they call it, is not only extremely useful but also profoundly fun.

• Mostly Harmless Econometrics
• Mastering 'Metrics

My final reference is Miguel Hernan and Jamie Robins' book. It has been my trustworthy companion in the most thorny causal questions I had to answer.

• Causal Inference Book

1. Introduction To Causality
2. Randomised Experiments
3. Stats Review: The Most Dangerous Equation
4. Graphical Causal Models
5. The Unreasonable Effectiveness of Linear Regression
6. Grouped and Dummy Regression
7. Beyond Confounders
8. Instrumental Variables
9. Non Compliance and LATE
10. Matching
11. Propensity Score
12. Doubly Robust Estimation
13. Panel Data and Fixed-Effects
14. Difference-in-Difference
15. Synthetic Control
16. Regression Discontinuity Design

If the notebooks won't load or you just want something better formated, I've uploaded all these tutorials to my blog