I had an awesome first day at @Zymergen and really looking forward to cranking out some models.

Teachers and students. UCLA is offering 3 courses in causal inference. The one in computer science is taught by Prof. Adnan Darwiche Here is a recording of his first two lectures: —Part A: https://t.co/Lk1bxkB08Z —Part B: https://t.co/CRHzmyDoer Enjoy.

We're hiring! I'm looking for data scientists and software engineers to join my team and to help us build out both our computer vision and chemical machine learning platforms. We also have an opening for a senior scientist. See https://t.co/fCbBAIPSjj for details and to apply.

sure ideas are cool but have you tried execution?

We are hiring Staff Engineers! (backend, frontend, mobile) Our notion of Staff Eng is heavily inspired by @Lethain's work on it. Specifically: Please refer anyone you know, RT, or DM me to learn more, or just apply here: https://t.co/RkQtBt43Hu

¿No les ha pasado que en entrevista te preguntan por la complejidad de tu solución y te hablan sobre "Big O Notation"? Bueno aquí les dejo una clase rápida de "Big O Notation"

My job is offering an 11-week internship program for anyone trying to get into Cybersecurity, especially if you do not meet the traditional background requirements. It’s fully virtual at $28/hr at no cost to you. Please reach out!

This coming Tuesday at 2-3:30PM PST @seanjtaylor and I will be presenting (in English) at the Nubank Data Science & Machine Learning Meetup, talking about causal identification to a data science/ML audience. If you're interested you can sign up here

Please RT! We have an online workshop "Causal inference from longitudinal data" from Mar 29-Apr 1. Highly interdisciplinary session with experts in neuroscience, psychology, political science, epidemiology & philosophy. Participation is free. https://t.co/cevEka9CCs (1/4)

I think a lot of people underestimate the liberal arts / philosophy value of Math, CS and Stats concepts. * Taylor Series (things don't have to be perfect to work) * Bandits/RL (earn/learn trade-off) * Confidence intervals (uncertainty drops with more data)

"OLS of Y on X is unbiased only if X is unrelated to the error e. If there's a term Z related to X in e, the sign of the bias is the product of the signs of Cov(X,Z) and Cov(Y, Z)" ??? "If Z hangs around X but OLS doesn't know about it, it'll give X all the credit for Z" "oh ok"

Let's put this in its proper scale and you'll see that it's an enormous effect: 70 minutes per day = nearly 6 hours per week. A typical work week is 40 hours, so we're talking about effective savings of 15% of the work week! HUGE GAINS.

ALMOST PERFECT and then I saw the *#&^&#$^ y-axis NOOOooOOO don't DO that to me!!

One of the first memes I share with students. I ask "Where are you in this picture? Are you in the box of memorizing formulas or are you in the play area of math exploration?" At the end of the course, I show this meme and ask the same question.

Really nice, humbling interview with the late David Cox by @learnfromerror https://t.co/7c8nYNNn3x

To all causality-minded readers who have been using Primer in the classroom and had trouble solving any of the homework problems - rejoice! We are now making the Solutions Manual available to all: https://t.co/Jx0L8KCsq7 So, let wisdom light the darker corners of our world.

Forks, Pipes, Colliders & Descendants Oh My: Elemental Confounds. Lecture 5 of Statistical Rethinking 2022. Gentle introduction to inferential effects of stratifying by variables in causal systems. Next lecture this week takes this do-calculus.

“More effort is wasted doing things that don’t matter than is wasted doing things inefficiently. And if that is the case, elimination is a more useful skill than optimization.” —James Clear

I'd take it back. There is a simpler example here: https://t.co/yA6cgmuh7m to illustrate why the combination of experimental and observational data can inform us about individual behavior. It shows how a simple observation can rule out the model of "no effect on any individual."

Wir alle, Bahn, wir alle