My Book Is Out! Why I Wrote It and How You Can Help

Bridging the Gap Between Deep Learning and Causal Inference—A Code-First Approach

It’s been a while since my last newsletter—and for good reason: I’ve been slaving away at a book. And now it’s out:

I wrote Causal AI to fill a gap. Deep generative machine learning and causal inference share a common foundation in probabilistic graphical models. Yet, despite deep learning’s explosive growth, its connection to causal reasoning has often been overlooked. This book bridges that gap.

It’s a code-first guide for data scientists, ML engineers, and researchers who want to integrate causal inference and deep learning—without needing to reinvent probability theory from scratch. It evolved from workshops I’ve run with teams at Google, Amazon, Nike, AstraZeneca, and more, all looking to apply causal AI to real-world problems.

If this sounds like something you (or a friend/colleague) would find valuable, please consider pre-ordering on Amazon—it really helps with visibility. And if you do pick up a copy, I’d be incredibly grateful if you left a review on Amazon or Goodreads.

Writing this book was a fascinating (and sometimes grueling) process. It was satisfying to get my ideas down on paper, but I won’t lie—debugging and formatting Pytorch code for the printed page SUCKED. People ask if I’ll write another. My answer? Maybe one of those stories about teenage love triangles with vampires and werewolves. If I’m going to work this hard again, I might as well get that Twilight money.

Now that the book is out, I’m excited to get back to regular newsletter-writing. More soon!