Methods of Causal Inference (From the Perspective of Econometrics and the Rubin Causal Model)

Here, I will organize articles and other resources, particularly from the Rubin Causal Model perspective. I will continue to add good articles as I find them. For an overview of causal inference as a whole, I have organized it below.

I feel that this has become popular alongside EBPM, but since I am a proponent of the Pearlian approach, these methods are likely standard for those who follow the Rubin Causal Model approach in econometrics.
Let's start with the easy-to-understand ones.

Resources that include sample programs, etc.
GitHub - yutatoyama/AppliedEconometrics2021: Applied Econometrics 2021

Well, I think that for practitioners, learning these topics is sufficient for an understanding of causal inference.

For topics like causal inference incorporating machine learning or meta-learners, which are starting to emerge but for which my understanding hasn't caught up yet, use these as an entry point.

I will move more textbook-like resources to the following.

I think I'll read a few more applied papers later.

If you would like to see other information, please go to the table of contents page.
Starting over by organizing collected information | Kusuguttagari | note