I don't know where do you get your energy to go round and round with that pair of midwits, Biostatsfun. I just saw a message with the same exact thing we went over last time: No, the g-formula is not the same as the do-operator.

This paper has been in the making for so long that eight children across the co-author group were born between the time we started and now! But it is finally out, and you can check it at arxiv.org/pdf/2503.13323

Why does linear regression minimize squared error? I always thought it was just computationally convenient compared to absolute values. But then I discovered it naturally emerges from maximum likelihood estimation with Gaussian noise assumptions 🤯 Same with regularization—L2/L1

Every Data Scientist should have basic Causal Inference understanding. Because without it, problems will be approached with models that will never be able to solve them. #CausalInference

One of the most underrated concepts in observational causal inference is sensitivity analysis. Sensitivity analysis helps you shift the conversation from “no confounder exists” to “it's unrealistic a confounder of strength X exists”. That’s a much more defensible claim.

About three weeks ago I gave a talk at Genentech, on "The Science of Cause and Effect, with a glimpse at Personalized Decision Making". I now have a video of the talk which I am happy to share: ucla.in/4jmj08c. I hope to see other drug-developing companies adopting

Haven't finished this one yet, but I highly recommend it and the other two courses by CausAI in Udemy.

Orthogonal to Frank Harrell’s initial note: CBN is a layer 2 model that lets us answer interventional (layer 2) queries using layer 2 calculus (do-calculus) -- see the 2nd green row in the attached table. One recent result: we can now more precisely match the query, graph, and

For the new kids in back: If you hate statistics, you'll love my free lectures. Putting science before statistics, from basics of inference & causal modeling to multilevel models & dynamic state space models. It's all free, made with love and sympathy. youtube.com/playlist?list=…

Natural consequence of the current times: a deluge of shit content crawling up on my book recommendations--GenAI shit books with GenAI shit covers. 271 titles by one Jamie Flux amazon.com/stores/Jamie-F… Anyone else experiencing this?

9 years later... It's still great. youtu.be/GDrBIKOR01c?fe…

The same with this one... 9 years after and so much of it stayed with me. As the final message, indeed somewhat made me. Thanks, Vsauce youtu.be/fCn8zs912OE?fe…

Launching Skolar: a new platform for hands-on, structured and certified training in open-source data science. eu1.hubs.ly/H0lk5kY0

❓Causal Inference Quiz Week – Day 2❓ This week is all about quiz questions related to causal inference, just for fun and learning purposes! Each day we’ll post one question. Answers (with explanations) coming on Friday. Have fun!

5/5 Last but definitely not least, I’m honored to be giving a keynote on Wednesday (7/23) titled "Towards Causal Artificial Intelligence." For details, see: auai.org/uai2025/keynot… Here’s a short abstract: While many AI scientists and engineers believe we are on the verge of

Bad "science", worst outreach

Finally, a good modern book on causality for ML: causalai-book.net by Elias Bareinboim. This looks like a worthy successor to the ground breaking book by Judea Pearl which I read in grad school. (h/t Joshua Safyan for the ref).