preface

I wrote this book because I wanted a code-first approach to causal inference that seamlessly fit with modern deep learning. It didn’t make sense to me that deep learning was often presented as being at odds with causal reasoning and inference, so I wanted to write a book that proved they combine well to their mutual benefit.

Second, I wanted to close an obvious gap. Deep generative machine learning methods and graphical causal inference have a common ancestor in probabilistic graphical models. There have been tremendous advances in generative machine learning in recent years, including in the ability to synthetize realistic text, images, and video. Yet, in my view, the low-hanging fruit of connections to related concepts in graphical causality was left to rot on the vine. Chances are that if you’re reading this, you sensed this gap as well. So here we are.