Great book that clearly explains the perils or inferring causation and now you can deal with them constructively.
For those who want more he has a more technical book "Causality: Models, Reasoning and Inference" which is also excellent.
Pearl is a legend in the field, who wrote the seminal "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference".
There is a school of thought that there is nothing new here. Pearl is very honest and open about who invented what and of the mistakes in his earlier work. While no one finding is entirely new here, the overall package adds up to a lot IMHO. And this after having intensively studied statistics and probability over many years. It really changed my approach to and understanding of causality. And most importantly it gave me reliable intuitions on the subject. After his books, things seem obvious to me that others struggle with.
I like Pearl's work a lot more than his opinions on who invented what, and who is right about what. For that reason, I would wholeheartedly recommend his books but I would also recommend skipping any sideshows about related literature.
Fact is, Pearl selectively picks literature concerned with causality and, in particular, literature not successfully tackling the subject.
He does ignore many other approaches to the issue, especially parallel developments to tackle problems in fields that he specifically critiques. In other words: Pearl, next to being a great researcher, is also a showman who knows how to build a following.
The essence of the debate is this: Neither Pearl's framework, nor anyone else's capture all valid approaches to causal inference. One can construct cases in Rubin's framework that DAG can not solve and vice-versa. The downside to Pearl's approach is that it is - right now - more difficult to implement. The cases where DAG undoubtedly succeeds better than other approaches are, in a sense, unlikely to succeed as a practical research projects.
That being said, a great strength of such graphical models is that they allow quite sophisticated reasoning in several well-known simple but non-intuitive cases. Such reasoning otherwise requires an immense amount of experience and / or education on the pitfalls of causal inference. That is also a reason why I would like to see this framework taught more in schools.
All in all, as another great post in this thread has pointed out, much of the debate is in violent agreement on base issues. Once this issue transcends the egos involved, much progress will be made and that is, in my view, very exciting.
I'll try to dig it up, saw it a while ago on a forum.
Another thing might be that in Rubin's framework it's immediately straightforward to do semi-parametric estimation and get consistency and all that. I'd say in practice that's probably not the first thing to do for DAGs, where writings are focused on toy models (the question then would be: how do I get to the correct DAG?).
Edit: This was posted itt, it has some examples of Rubin's framework (potential outcomes) that can not be identified in the DAG framework
https://arxiv.org/pdf/1907.07271.pdf
For those who want more he has a more technical book "Causality: Models, Reasoning and Inference" which is also excellent.
Pearl is a legend in the field, who wrote the seminal "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference".
There is a school of thought that there is nothing new here. Pearl is very honest and open about who invented what and of the mistakes in his earlier work. While no one finding is entirely new here, the overall package adds up to a lot IMHO. And this after having intensively studied statistics and probability over many years. It really changed my approach to and understanding of causality. And most importantly it gave me reliable intuitions on the subject. After his books, things seem obvious to me that others struggle with.