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Thank you for purchasing the MEAP for Causal AI. You’ll want to have established skills in scripting data science analyses to get the most benefit from this book. You should also know basic probability concepts such as conditional/joint probability, expectation, conditional independence, and Bayes rule. The book provides a review of these concepts.

My path to causal inference was through computational Bayesian statistics and probabilistic machine learning, techniques that, at their core, are about simulation. I found it easier to understand hard ideas when I could simulate them with code. If you are the same, this book is written for you.

When I entered the tech industry, I was surprised to find that causal inference practitioners weren’t linking probabilistic machine learning and graphical causality. Instead, for historical reasons (tech companies had hired economists to tackle causal inference problems), causal inference was presented as a laundry list of applied statistical and econometric methods. So I started learning these techniques, and it immediately felt like a Sisyphean task. That’s saying something, considering I already had a Ph.D. in statistics and had already spent years working on causal inference. If you’ve tried to learn causal inference and it also felt like drinking from a statistical firehose, this book is written for you.