Causal Inference - Learning Introductory Path (Website and Code)

        In recent projects, more and more [causal inference] projects have been encountered. Ordinary AI (neural network) can only predict correlation, not causation, which severely limits the usefulness of the model. Therefore, I would like to recommend the following articles and packages to everyone for the scenario of [causal inference]

Recommend an introduction article Introduction to Causality (qq.com)

Matheus Facure's github personal website:

matheusfacure (Matheus Facure) (github.com)

1) Introduction to causal inference - why causal analysis is needed
https://matheusfacure.github.io/python-causality-handbook/landing-page.html

2) Causal Inference in the Age of Machine Learning AI
https://econml.azurewebsites.net/spec/estimation/dr.html

3) Causal inference python package produced by Uber
https://github.com/uber/causalml

4) Based on the actual combat example of 3)

5) The following is the code download URL:

matheusfacure/python-causality-handbook: Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality. (github.com)