iiinova — where your digital self keeps evolving

Boris Sobolev @sobor.bsky.social · 24/12/2024

sometimes, a flower is just a flower

Boris Sobolev @sobor.bsky.social · 23/12/2024

Un laberinto de tentaciones

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Boris Sobolev @sobor.bsky.social · 21/12/2024

If you've never tasted the dishes prepared by la chefa Anayeli Suazo of Tentaciones, you don't know what a gourmet meal is and what it looks like.

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Boris Sobolev @sobor.bsky.social · 20/12/2024

Nothing can beat sleeping until noon on vacation

Boris Sobolev @sobor.bsky.social · 14/12/2024

I had a quick chat with Elias Bareinboim on the way from the a/p #neurISP24… He reminds me of Mozart, who barely has time to write down all the revelations of causality pouring on him from above. 😊

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Boris Sobolev @sobor.bsky.social · 23/11/2024

Wondering about #causal mediation analysis in comparative effectiveness research? Check out our framework for the mediating effect of treatment time of 2 treatment methods for complex coronary artery disease. doi.org/10.9778/cmaj...

There’s this misconception that 'causality is in the design.' Apart from being uninformed, this assertion is fed by a bigger fallacy: that causality is a product of our mind rather than a feature of the material world. Pearl’s major contribution is grounding causality in materialism.

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Oh, I agree that causal claims come from reasoning, and that design is a part of it. I just don't see how reasoning using words can be superior to reasoning using mathematical notation. Pearl’s formulas easily describe RCT. But trialsts have a hard time to describe the estimand of their experiment.

So, you admit that your notation doesn’t capture the difference btw association and causation? And! and your reasoning ‘it’s in the design’ is not reflected in your notation, right?

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I’m glad that you are using a rigorous notation for statistical hypothesis testing. Because it offers me an opportunity to ask, where is causality here?

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Notation gives us a means to express our ideas in rigorous and transparent fashion. Whether it is for radius and circumference, or for a value that outcome variable would assume in unit u, if it were assigned to a treatment group regardless of any factors that might influence treatment assignment.

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You mean, this Fisher? 👇🏻 A denier of causal relationship btw tobacco smoking and lung cancer? yet ‘seeing’ causality btw race and the quality of ‘human stock’? Btw ‘high intellectual calibre’ of parents and their offsprings? Inverse link btw ‘fertility’ and ‘achievement’ of most valuable classes?

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The most amazing part of the EBM proselytism is that it cannot specify the estimand of an RCT. The Pyramid evangelists keep repeating with religious fervor that the causal effect is in the design, but they refuse to reveal what the causal effect is. Credo quia absurdum

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There’s this misconception that causal evidence comes from data. It comes from reasoning. Unless, of course, the High Priests of EBM Pyramid deny us mere mortals the ability to think.

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I’ve always been puzzled by my aversion to interpreting RCT outputs as evidence. Finally, I figured it out. An RCT’s output, vaunted as assumption-free, are in fact not falsifiable. There is nothing to falsify. That what makes EBM Priests so pompous: they present infallible evidence.😂

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I agree. The PO notation is not needed to understand the counterfactual nature of the question that a randomized experiment purports to address. The notation of Pearl’s First Principle would suffice.

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When I hear 'I don't like Pearl's causal hierarchy', it reminds me of an old story. A tourist at the Louvre looks at La Gioconda and declares: 'I don't like it!' The curator nearby responds: 'Oh, she's seen so many people over the centuries that nowadays she decides who gets to like her.'

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The evangelists of EBM gospel fail to realize that the ATE in randomized trials not only involves counterfactuals, but actually answers a counterfactual question.👇🏻

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There’s this misconception that probability of diverging outcomes of alternative treatments is just a theoretical consideration Here’s the probability of benefit (survival if treated early, mortality if not) estimated empirically. Each bar shows the range of its possible values.

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The types of questions we ask determine the types of answers we get. 👇🏻

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Amazing! Pearl’s probability of causation coincides with the causal effect P(Y_x) - P(Y_x′) under some conditions. ie, the joint probability of opposing counterfactuals P(Y_1=1 & Y_0=0) equals the difference of marginal probability P(Y_1=1) and, wait for it, P(Y_0=1)

Please don't miss @eliasbareinboim chapter on @yudapearl causal hierarchy. I highly recommend it for the authors’ effort to give consistent notation, thoughtful examples, crisp statements. Such a powerful reading material that we can use in educational efforts.

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