python-causality-handbook
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matheusfacure
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
Love the book so far :) I noticed that there wasn't any mention of the definition of conditional probability, i.e. "expectation of Y given T = 1" or the definition...
Greetings Mr.Facure, Thank you for writing this wonderful book and making it freely available online ! It's very nicely written, super informative, I learned a lot while also enjoying reading...
I'm wondering do I need to use ipw for xlearner, as the last step of xlearner has used ps value to re-weight sample? Need help for this question, thanks!
First of all, thank you for making this very accessible book! In the section about continuous treatment in chapter 20, you defined ```math Y^*_i := (Y_i- \bar{Y})\dfrac{(T_i - M(T_i))}{(T_i -...
There is a typo in Chapter 01, Header "Data Science is Not What it Used to Be (or it Finally Is)": "Second, learn what makes your work valuable and valuable,...
There is an issue on chapter 21, in the following paragraph from nb21 import cumulative_gain, elast when pip install nb21， no matching distribution found for nb21,can you tell me why...
Chapter 18 was updated in previous commits to use "sensitivity" instead of "elasticity". The code was updated, but the corresponding outputs weren't. I updated the output dataframes and plots that...
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Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.