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Boris Sobolev

@sobor.bsky.social

600 followers 56 following 75 posts

Causal questions, counterfactual answers

Economics 35%

Medicine 31%

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Boris Sobolev @sobor.bsky.social · Jan 18

ATE? nah…
We ask how many patients would get better with treatment but not without it.
youtu.be/ms4z2i7JzVY

Probability of benefit in clinical practice

YouTube video by SobolevSpaces

Boris Sobolev @sobor.bsky.social · Mar 8

Ce que l'on conçoit bien s'énonce clairement

ну не дано тебе, ну нету дара… пора переквалифицироваться в управдома…

Boris Sobolev @sobor.bsky.social · Mar 8

‘the vision based on evidence’?! 🤡

keep digging; 🍿

Boris Sobolev @sobor.bsky.social · Jan 26

Every joke has some fun in it. ☺️

Boris Sobolev @sobor.bsky.social · Jan 26

For the same reason trialists are so excited about not knowing treatment mechanisms: they want to get away with ATE whereas patients expect clinical decision-making based on ITE.

Boris Sobolev @sobor.bsky.social · Jan 25

Вечер перестал быть томным.

Boris Sobolev @sobor.bsky.social · Jan 25

@pwgtennant.bsky.social Peter, your repost should bring us who study SES in health to re-evaluation of how we do research. We should focus on causal questions, causal methodology, DAGs, probability of benefit, recovery from selection bias, transportability of effects, causal fairness.

Boris Sobolev @sobor.bsky.social · Jan 25

hold on, hold on, we’ll get to foolishness in a moment.

In Fisher 1926, you read ‘averages’.
A question for an undergrad completed an elementary course in statistics — To which group of statistical objects do averages belong: estimators or estimates?

Boris Sobolev @sobor.bsky.social · Jan 25

Great! I’m glad you agreed it’s ‘concluded’…
That’s right, it is not just an estimator.

Boris Sobolev @sobor.bsky.social · Jan 25

Indeed, this should be engraved at the entry to every EBM office: 'Randomization ensures a valid error estimate. This may be applied to test the significance of observed difference btw averages of the treatment groups.'

Not a word about causality, as it is concluded by reasoning, not statistics.

Boris Sobolev @sobor.bsky.social · Jan 24

Oh, by all means, please test the significance of the observed difference between the averages of plots treated differently. 😂

Average here, average there.

Boris Sobolev @sobor.bsky.social · Jan 24

Probability of benefit, PNS, is the future that personalized medicine is going to discover…

Boris Sobolev @sobor.bsky.social · Jan 24

According to Fisher, it’s about ‘testing observed differences between average outcomes of groups treated differently’

Boris Sobolev @sobor.bsky.social · Jan 24

Fisher said many things, including about eugenics and tobacco smoking.

But he didn’t say that thing you attributed to him.

Boris Sobolev @sobor.bsky.social · Jan 24

I am not.
I am an arrow.
I am a straight line going beyond the EBM horizon.

Boris Sobolev @sobor.bsky.social · Jan 24

On the right, I see an estimator, which you reason is unbiased due to design. Fine. What is the estimand? What is it estimator of?

Boris Sobolev @sobor.bsky.social · Jan 24

again, what makes it causal? all the probabilities here are conditional 🤷‍♂️

Boris Sobolev @sobor.bsky.social · Jan 24

If one can’t explicitly demonstrate the target quantity of measurement, how do they know they measure it?

That tells all about the scientific pretensions of EBM preaching

Boris Sobolev @sobor.bsky.social · Jan 24

Like we say in Russia: на воре шапка горит… 🫣

agreed, the preaching of EBM High Priests is totally useless in determining causal estimands

Boris Sobolev @sobor.bsky.social · Jan 24

A sleight of hand, again? ay-ya-yay…

Do they, in experimental design, teach you machinations?

Just put it down, in math notation or layman words, the causal estimand of an RCT… not reasoning, not statistical phraseology, not EBM blurred vision, not Rubin’s charade; just an estimand… then we talk

Boris Sobolev @sobor.bsky.social · Jan 24

Are those the same books that make you struggle with writing down a causal estimand?

It reminds me an old joke about Communism: ‘We promised you the bright future; no one was promising food and shelter’

Boris Sobolev @sobor.bsky.social · Jan 24

I can recommend a great optometrist.

Boris Sobolev @sobor.bsky.social · Jan 23

It’s a free country, you can fancy any name. But that’s just a name in your head.

Cause-and-effect relationships is a feature of this world. Causal inference is how we convince ourselves and others that changing X will change Y.

Boris Sobolev @sobor.bsky.social · Jan 23

I thought we already agreed that ‘sleight of hand’ is not admissible in our debate 🫢
My example of a random function gives you several values for the same element from the probability space and shows that your animus toward ‘counterfactuals in causality’ is simply misguided…

Boris Sobolev @sobor.bsky.social · Jan 23

‘very’ bad? is the adverb really necessary?

are you saying you can’t define a family of random variables on the same probability space indexed by t from the value set T?

Perhaps some elementary textbook of probability can be useful…

hé hé

Boris Sobolev @sobor.bsky.social · Jan 23

Well, math makes no sense to those who don’t know it, right? For the rest of us, a random function would suffice… no counterfactuals, no strawman, no crows, no windmills… 👇🏻

Boris Sobolev @sobor.bsky.social · Jan 23

oh, wait a sec… are you arguing with Pearl here?

or, just pulling of
the ‘strawman’ argument?

it is kinda lame…

I thought we were having a genuine debate: you and I, man to man…

Boris Sobolev @sobor.bsky.social · Jan 23

ah, you transitioned from math notation to pics 😊… ok, let’s do a pictorial argument

Boris Sobolev @sobor.bsky.social · Jan 23

Who said anything about counterfactual here? 🙄

“En resolución, él se enfrascó tanto en su lectura, que se le pasaban las noches leyendo de claro en claro, y los días de turbio en turbio; y así, del poco dormir y del mucho leer, se le secó el cerebro de manera que vino a perder el juicio.”

Boris Sobolev @sobor.bsky.social · Jan 22

I’m glad you asked!First, ‘observational’ refers to the method of assessment, right?
Since John Stuart Mill, we define individual causal effect in unit u as Y(1,u) - Y(2,u), where Y(x,u) is the state of unit u treated with x. Huzzah!

Boris Sobolev @sobor.bsky.social · Jan 22

Sleight of hand, my friend? 😂 Statistics and communication aren't the same either, yet you're happy to use H0 symbol and the concept of conditional expectation. Humans are good at causal epistemology, because we observe and experiment, conceptualize and develop symbols.