Selected papers Timescale: https://t.co/Ul97J0rjpR DCMs: https://t.co/kRlhGhkIId Discrimination: https://t.co/U5lXnpecIa Mediation: https://t.co/83LlzrNCBS Faithfulness: https://t.co/ASuhs835wI Causation and Time: https://t.co/FgHgy3GiNf Psychometrics:

A sneak peak for next week's episode: Does Causation Imply Correlation? https://t.co/zaLLl2tAWd

I'm excited to post this new episode on the causal Markov condition! https://t.co/inqQBT7qex

I just posted a video on the Front-Door criterion, which enables you to identify an effect using mechanisms. This video is more technical than the others, but really illustrates how causal graphs create a bridge between causes and probabilities. https://t.co/H8E11CEZyT

After a bit of a summer pause, I'm back to making episodes. In this episode, I explain the notion of confounding, and clarify why confounders should not be thought of as alternate explanations of an observed effect. https://t.co/3Ml9bxNgVy

This clip is the part of the last episode that has so far generated the most discussion. https://t.co/7RUcH4Pmj0

I decided to make an intro episode, to provide some orientation for the later episodes. In it, I introduce DAGs and explain how causation is a strategy for managing complexity. I spent a lot of time improving the sound quality, so I hope that paid off. https://t.co/G9ph8wgFhU

How can a drug raise the chance of recovery in a population, while lowering the chance in both men and women? The answer is Simpson's paradox, which still confuses people. Here I clarify it, w/ examples from COVID-19, and testing police discrimination. https://t.co/b8ApocTFnl

Coming next week on Causal Foundations: Simpson's Paradox! Subscribe to my YouTube channel in order to not miss episodes when they're released. https://t.co/Ac1uD74rWt

Episode 3 is now up. In it, I discuss what the most underrated concept in causal inference: identifiability. It's what enables one to measure causal effects from data. I'm still figuring out the AV side of things. Feedback welcome! https://t.co/it11A9SNH7

Episode 2 of Causal Foundations is now online. Thanks to everyone for the very encouraging reactions to the opening video of the series. https://t.co/9GGf6tIKne

While trying to figure out how searchable my channel is on YouTube, I've stumbled across videos of talks I didn't know were there. Here's a talk I gave at Yale, recorded by a professional film crew and well summarized in the description. https://t.co/UHpgOilYZd

Episode 2 of Causal Foundations will be released on Monday. Subscribe to my channel be notified when it is posted. https://t.co/h43r6Kk3nG

I decided to make a youtube channel in which I introduce key concepts from causal inference in videos of roughly 10 minutes. The first episode "Correlation Does Imply Causation" is now online. Feedback would be very welcome. https://t.co/8XAIIPaNx8

70% of physics is breaking down the system into a simple harmonic oscillation

Very cool! There's definitely a strong connection between my paper and RL, and in the paper I only allude to it. Would be thrilled to discuss it further.

This might be recency bias, but I think this is the best paper I've written. It showcases how causal models can unite engineering, dynamic systems theory and biology, while pointing to the work to be done in learning and applying causal concepts. https://t.co/6JWXxNutf6

Thanks, Alex, for the shoutout. I'm glad you liked it.

Ok. So the 24-30 month olds got it. Now let's work on the statisticians.

Thanks for two and a half exciting days at CEPDISC’24 last week exploring discrimination in general and discrimination from the perspective of social psychology and philosophy in particular. Thank you to all the incredible speakers and participants who joined CEPDISC'24.

I'll be curious to read this paper referenced today by @DouthatNYT & welcome comments from experts on the topic. To me it seems like a single paper (draft?) doesn't justify the "almost certainly" claim, though I guess nothing in this domain is settled. https://t.co/VzXHptqcnm