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I'd take it back. There is a simpler example here: discourse.datamethods.org/t/individual-r… 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."

“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

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. youtube.com/watch?v=UpP-_m…

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: drive.google.com/file/d/1nP9LxO… So, let wisdom light the darker corners of our world.

Really nice, humbling interview with the late David Cox by ♕Deborah Mayo♕ errorstatistics.com/2022/01/22/a-i…

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.

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

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.

"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"

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)

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. longitudinaldataanalysis.com (1/4)

This coming Tuesday at 2-3:30PM PST Sean J. Taylor 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 meetup.com/pt-BR/machine-…

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!

¿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"

sure ideas are cool but have you tried execution?

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: youtube.com/watch?v=ci3muJ… —Part B: youtube.com/watch?v=MKrUUM… Enjoy.

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