“Causal Inference in Python” Author Interview and Reading Notes
📚 Some people may know that I host a DS/ML book club where we read one book per month. This month, we read ‘Causal Inference in Python’ by Matheus Facure. Matheus is an expert in causal inference and wrote the well-known book ‘Causal Inference for The Brave and True’ in the past.
The book covers various important concepts and methods in causal inference, such as ATE, ATT, ITTE, propensity score matching, synthetic controls, difference-in-differences, meta-learners, treatment heterogeneity, instrumental variables, and discontinuity design. Each method is explained with an example from the industry to make it more relatable.
In this blog post, I’d like to share an interview we had with the author that’s full of insights. I’ll also be sharing my personal reading notes for those who are curious about this book.
🔗 Book link: https://amzn.to/3sD0bs8
Our DS/ML book club had a wonderful time chatting with Matheus about this book. Here are some key points from our discussion:
- Cross-validation in causal inference.
- ATE vs. ATT
- Synthetic control: how it’s related to time-series forecasting
- Causal reinforcement learning
- Industry use cases and side projects for causal inference
- Future of causal inference