A non-complete list of books to understand causality from Pearl and Rubin's perspective.

• The book of why: The new science of cause and effect
        by Judea Pearl and Dana Mackenzie, 2018. Get Book
  [Must Read] An amazing beginner's guide to graph-based causality models.

• Causal inference in statistics: A primer
        by Madelyn Glymour, Judea Pearl, Nicholas P Jewell, 2016. Get Book
  [Must Read] The essense of causal graph, adjustment, and counterfactuals in FOUR easy-to-follow chapters.

• Causality: Models, Reasoning, and Inference
        by Judea Pearl, 2009. Get Book
  [Suggested] A formal and comprehensive discussion of every corner of Pearl's causality.

• Causal inference in statistics, social, and biomedical sciences
        Guido W Imbens, Donald B Rubin, 2015. Get Book
  [Must Read] A formal and comprehensive discussion of Rubin's potential outcome framework.

• Causal Inference for The Brave and True
        Matheus Facure, 2021. Get Book
  [Must Read] A new book that describes causality in an amazing mixture of Pearl's and Rubin's frameworks.

Not necessarily books. Posts and papers are included.

• Resolving disputes between J. Pearl and D. Rubin on causal inference [Go to post]
  [Must Read] The post from Prof. Gelman shows the disputes from Rubin's perspective. It helps understand why Pearl's framework faces great challenges in the statistic community while being so successful in machine learning and social computing.

• “The Book of Why” by Pearl and Mackenzie [Go to post]
  [Must Read] Critics from Rubin's causal perspective to the famous guiding book for causality: The book of why.

• Chapter 8, The Book of Why? [Get book]
  [Must Read] Pearl's overall discussion of the short comings of Rubin's potential outcome framework.

• Can causal inference be done in statistical vocabulary? [Go to post]
  [Must Read] Pearl's initial reponse to Gelman's critics on The book of why.

• More on Gelman’s views of causal inference [Go to post]
  [Must Read] Pearl's next reponse to Gelman's critics on The book of why.

• Many thanks to my advisor Prof. Zhenzhong Chen for his mentoring and guidance.
• Many thanks to Prof. Yongfeng Zhang for his non-withholdingly sharing of his knowledge in causality.
• Thank my teammate Xubin Ren, Jing Yi, Jiayi Xie for their constructive discussions.

Deeply appreciate it if you have more resources to add to the list. If you'd like to contribute, fork the repo and start a pull request.