Tentative answer: The longer than usual trip prompts us to incorporate more variables in our model, we discarded smoke on the horizon in our decision (model) previously, next time we will not. Not clear how to formalize this idea.

it is not clear to me how this process takes place within the SCM formalism, any pointers? 3/3

From Primer chap.4: with the information I have now, had i taken the freeway (instead of Sepulveda), I would have gotten home earlier. What is the point of making this assessment if not to update our model to make a better decision next time? 2/3

As a fully open LLM, Apertus allows researchers, professionals and experienced enthusiasts to build upon the model and adapt it to their specific needs, as well as to inspect any part of the training process. Watch our video:

Thank you! The JTBD Pyramid is (still) Amazon's #1 New Release in Product Management, by @newproductdog https://t.co/ak89UYhKPd

And I don't mean asymptotically... No matter the size we can always consider more covariates such that there will be imbalance between treatment and control (all the genes and history of a subject for example). If so how might we explain this? "sum of many effects"?

So to be clear the relationship between G and Y_1 and Y_2 is not causal. So this is a question about let's say conversion between graph representations... If you have any literature you could point to that would be greatly appreciated! Thanks! 2/2

Il est minuit moins cinq avant l’apocalypse technologique de l’Europe. Et je pèse mes mots. L’obsession pour la régulation, le mépris des élites technocratiques envers les entrepreneurs et la peur de l’avenir nous font décrocher de la grande bataille de l’IA. Pendant que

so to be clear the reduction we find in this case is potentially mainly due to a regression to the mean effect rather than the causal relationship between T an Ynow

Some fantastic work presented here, including a do-calculus for level 3 of the ladder of causation by @eliasbareinboim !

Ok I think I understand: IF people can have diabetes AND another disease, this means that, due to the strong probability of diabetes when overweight, we will observe many people with both diabetes and these symptoms even though these symptoms aren't caused by diabetes?

I don't understand why the bayesian approach would give high prob of diabetes if we condition on those symptoms?! prob of having those symptoms and diabetes would be low no? 2/2

Nice connection between causality and storytelling!

Get jobs-to-be-done from Urko Wood, who specializes in working with executives... on the latest Product Quest Podcast @WermuthYann @jonathanedwards https://t.co/EErbum0fK3

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For a better world… we need to Free Julian Assange

Less than a week since OpenAI started rolling out ChatGPT vision. And people have been busy executing god-like tasks. 35 truly mind-boggling examples: (29th is my favorite)

I gave ChatGPT a screenshot of a SaaS dashboard and it wrote the code for it. This is the future.

Latest episode is out!

Interesting tidbit from @sama on the role of Why Greatness Cannot Be Planned in the history of OpenAI. (thanks to @actualMahmoud for highlighting)