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I have almost finished studying 'Causal Inference in Statistics: A Primer', but I still feel that I need to learn more.
I considered 'Causality' (Pearl, 2009), but there seem to be several good learning resources about DAG (ex. Review Paper & etc).
What should I study after finishing 'Causal Inference in Statistics: A Primer'?

Richard Hardy
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vinsh_77
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    Cunningham's The Mixtape book or Huntington-Klein's The Effect book would be suitable suggestions for a new starter. (Both books are free online too.) For something more formal would be the What If book from Hernan & Robins and Elements of causal inference by Peters et al. These four freely available books would most likely cover the vast majority of CI use cases one come across. – usεr11852 May 28 '22 at 11:56
  • @usεr11852 Thank you for answering my question. I interest 'Directed Acyclic Graph' for data analysis in Web Platform Business(ex. SNS, Commerce etc). I've been interested in the book 'Elements of causal inference' ever since. Can you give me a rough idea of what the 'Elements of causal inference' book contains? – vinsh_77 May 28 '22 at 12:14
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    It is the most advanced of the four books mentioned so I am not sure it is very constructive without enough background. (I am also not a huge fan of having very few exercises.) Dr. Peters has some excellent YouTube content, for example, the series here - probably go through those first and refer to the book later. I know it is "uncool", but if you are interested in business applications do look at Econometrics literature. Pearl's work is not in a vacuum. That group has a lot of causal inference mileage. – usεr11852 May 28 '22 at 13:06
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    (If you want to do "proper" DAGs, do check Probabilistic Graphical Models by Koeller and Friedman. It is exceptional for the mathematical/theoretical background.) – usεr11852 May 28 '22 at 13:11
  • @usεr11852 Thanks so much for answering my question!

    I'm not sure, but Elements of Causal Inference draws me in because it looks identical to Pearl's DAG perspective.

    I have another question.

    1. What is 'proper' means? (I am not a native English speaker, so I do not understand the pronunciation of words well.)
    2. How many preliminary knowledge of mathematics and statistics do i need for studying 'PGM'?
     – vinsh_77 May 28 '22 at 14:12
  • https://stats.stackexchange.com/questions/568281/is-there-any-theory-or-field-of-study-that-concerns-itself-with-modeling-causati/568294#568294 – Adrian Keister May 28 '22 at 15:27
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    Sure, hit it. As mentioned probably do the lecture first, I found them very informative. 1. Regarding "proper": apologies, I meant it instead of "really good", it has informal use like: "Kevin is a proper player". I meant it provides a clear and rigorous foundation for the use of Bayesian Networks. 2. Pff... For PGM probably a class in Probability/Mathematical Statistics already. It is not meant to be an intro and neither is the Elements of Causal Inference. – usεr11852 May 28 '22 at 15:29
  • Also consider Brady Neal's 'Introduction to Causal Inference' lectures on Youtube, with an accompanying free ebook (although the final chapters are still incomplete). – mspices Apr 19 '23 at 16:24

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