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FYI there's (at least?) two Judea Pearl books on causality. "Causality" was written in early 2000s, and is filled with theorems + proof + prose detailing all the major results from the theory. It's not a particularly hard book if you studied Mathematics, but it is Mathematics.

"Causal inference in statistics" is a 2017 book that covers the actually important results from the previous book, in an approachable manner, and with a focus on applying the results to actual problems. Some theorems are stated formally, but usually without proof. It's 40$ on Amazon, strongly recommended. https://www.amazon.com/Causal-Inference-Statistics-Judea-Pea...

There's also "The Book of Why", which is a pop-sci-ish book by Pearl and Dana Mackenzie. It contained just enough math and examples to get me really excited for causal inference, so I just bought "Causal inference in statistics" to see the theory in detail. If you want to learn what causal inference is about, but don't necessarily want to wade through a textbook immediately, I highly recommend "The Book of Why".


If anyone's reading the first of those two, make sure to go straight to the last chapter or epilogue or whatever it was. IIRC, it's basically a talk turned into a chapter for the layman. This might only be the in the more recent edition(s).

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