Boris Sobolev @soboleffspaces
š¦š°šµš¼š¹š®šæ/ššššµš¼šæ/š§š²š®š°šµš²šæ ā¢ causality in plain language ā¢ Vancouver Joined December 2019-
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Gladly agree. SCMs represent the world as a cascade of responses to changes in its state, where the output of each step serves as the input to the next for a given study unit. The probability distribution over the unit space links the SCM to data.
Gladly agree. SCMs represent the world as a cascade of responses to changes in its state, where the output of each step serves as the input to the next for a given study unit. The probability distribution over the unit space links the SCM to data.
Both causation and covariation manifest themselves as associations. Just because some features drive the black-box predictions doesnāt mean changing their values will actually change the outcome in real life. Kinda like that old joke ā nobody ever promised to feed the masses inā¦
Both causation and covariation manifest themselves as associations. Just because some features drive the black-box predictions doesnāt mean changing their values will actually change the outcome in real life. Kinda like that old joke ā nobody ever promised to feed the masses inā¦
Thank you Elias for pushing our thinking this direction, and please pass my admiration to Drago for his productivityā¦ Iām wondering why did you use another LLM as the ground truth here? it just adds another abstractionā¦ I mean, wouldnāt it be more convincing to estimateā¦
Thank you Elias for pushing our thinking this direction, and please pass my admiration to Drago for his productivityā¦ Iām wondering why did you use another LLM as the ground truth here? it just adds another abstractionā¦ I mean, wouldnāt it be more convincing to estimateā¦
Iām thrilled to share that the Second Edition of The Book of Why will be released at the end of this year. It will include brief discussions of recent breakthroughs in causal inference, as well as some aspects of LLMs. Join me on this next journey into the land of causality āā¦
Iād go even further than Schopenhauerās: āMan can do what he wills, but he cannot will what he wills.ā Ideas are determined; ideas donāt determine.
Iād go even further than Schopenhauerās: āMan can do what he wills, but he cannot will what he wills.ā Ideas are determined; ideas donāt determine.
āEvidence-based enterpriseā turns science into clickbait journalism; headlines get crowned as truth and then swung like a machete in every argument.
āEvidence-based enterpriseā turns science into clickbait journalism; headlines get crowned as truth and then swung like a machete in every argument.
Intelligent agents that canāt plan? And just leave planning to programmer? š That doesnāt sound like free willā¦ or agency.
Thatās not correct! šš» The assumption is much milder. We just need to assume that the factors we condition on (or stratify on, in epi lingo) block all chains of dependency that could connect the exposure and the outcome. A minimally sufficient adjustment set may not includeā¦
Thatās not correct! šš» The assumption is much milder. We just need to assume that the factors we condition on (or stratify on, in epi lingo) block all chains of dependency that could connect the exposure and the outcome. A minimally sufficient adjustment set may not includeā¦
@AdanBecerraPhD @soboleffspaces @yudapearl Yes a counterfactual mediation analysis was used to estimate the Natural Direct Effect of recognition in out of hospital cardiac arrest. Example: pubmed.ncbi.nlm.nih.gov/37453691/
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 ā all in the context of cutting-edge clinical practice! @yudapearlā¦
Exciting breakthrough from @eliasbareinboim -- a counterfactual-calculus (cfl-calculus), akin to do-calculus, for handling identification problems in Layer-3 of the Causal Hierarchy.
Exciting breakthrough from @eliasbareinboim -- a counterfactual-calculus (cfl-calculus), akin to do-calculus, for handling identification problems in Layer-3 of the Causal Hierarchy.
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