Boris Sobolev
@soboleffspaces
𝗦𝗰𝗵𝗼𝗹𝗮𝗿/𝗔𝘂𝘁𝗵𝗼𝗿/𝗧𝗲𝗮𝗰𝗵𝗲𝗿 • causality in plain language •
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Opioid prescribing in Canada: a continued retreat from God’s own medicine Thank you @DavidJuurlink for the insightful commentary! “Laypeople and clinicians alike often view opioids as the analgesic gold standard, even though rigorous studies show nonsteroidal anti-inflammatory…
![soboleffspaces's tweet card. [See related article at www.cmaj.ca/lookup/doi/10.1503/cmaj.250670][1] Key points In this issue’s research article, Rebić and colleagues show that rates of opioid prescribing across 6 Canadian...](https://pbs.twimg.com/card_img/1981057101565952000/a1QA_wLB?format=jpg&name=orig)
Exactly! The historical struggle of statistical reasoning with causation shouldn't distract even the most brilliant statisticians from acknowledging that estimand AIN'T estimator. CI is after the causal estimand (ATE/Pr benefit) and identification assumptions. Estimators…
Progress!!! @f2harrell agrees that the philosophy of causal inference (CI) is necessary for trialists to follow. He adds that it is not sufficient but does not explain why it "does not lead to minimal bias estimators" when it out-sources the estimation job to the most brilliant…
The field’s leading figures, say @eliasbareinboim @murat_kocaoglu_ are AI researchers themselves. @eliasbareinboim even wrote a textbook Causal AI. No surprise, it’s based on Pearl’s SCM and Causal Hierarchy. ‘This textbook offers a comprehensive treatment of the principles,…
My plea is that you, the CI field, devote 30% of the energy spent arguing with clinicians and econometricians over trivial problems toward engaging with statistical learning theorists and AI researchers.
Happy to share the link to the published dissertation @JacobJHutton open.library.ubc.ca/soa/cIRcle/col…
Congratulations to @JacobJHutton on a successful defense! What a feast for causal inference: causal diagrams, minimal adjustment sets, mediation analysis, direct and indirect effects, probability of benefit — all in the context of cutting-edge clinical practice! @yudapearl…
I wanted to present your work as an epistemological project; and to move beyond the bad infinity (die schlechte Unendlichkeit) of twittering about 'finite samples' and 'missing-data problem'. AI advances have brought us back to the real questions: What is knowledge? How do we…
I truly appreciate your clear summary of Kant's contributions to epistemology. I never understood why philosophers revere him the way they do -- now I do. @soboleffspaces
I’m a Pearlian! I follow and advocate for the epistemological approach of Judea Pearl. You can call me Жемчужников.
So are you a realist then? Again, I said framework. The what about it is what would your answer be. If the way I phrased it was imprecise, okay. But if not, your reaction is curious. The fact that regression was created at all would imply it offered some utility.
While bureaucrats and the “concerned citizenry” panic about chatbots, LLMs are busy doing what they can’t — making us more productive. Here’s a figure Overleaf generated from LaTeX code that Gemini 2.5 Pro writes from my verbal (3 sentences) description of this NCM architecture!…
And! Not only did causal theory @yudapearl lay the foundation for the “how” in mediation, fairness, and counterfactual analysis. It also gave us the “how” of Causal AI, opening the era of computable individual causal effects! In the process, it threw the regression…
Is it just me, or is “IRL-FAFO-headcanon” basically Pearl’s “seeing-doing-imagining” in Zoomer lingo? 🤭
If causal knowledge only comes from experiments or experience, then it just tells us how things happen, not how they have to be. But somehow, do-calculus and CTF-calculus manage to be pure reasoning, and still tell us what to expect in the real world.
Frank, a fair challenge in public space: a table in which causality concepts are expressed side by side in the language of DOE and SCM.
You are talking as though causal inference is opposing "experimental designs" etc. -- It isn't. Causal Inference is a language with which one can talk coherently and transparently about all aspect and cause-effect relationships.
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CAUSALab
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Sander Greenland
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