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.
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.
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.
- Introduction To Causality
- Randomised Experiments
- Stats Review: The Most Dangerous Equation
- Graphical Causal Models
- The Unreasonable Effectiveness of Linear Regression
- Grouped and Dummy Regression
- Beyond Confounders
- Instrumental Variables
- Non Compliance and LATE
- Matching
- Propensity Score
- Doubly Robust Estimation
- Panel Data and Fixed-Effects
- Difference-in-Difference
- Synthetic Control
- 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