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In 1921 [[Sewall Wright|Wright]]'s [[Path analysis (statistics)|path analysis]] became the theoretical ancestor of causal modeling and causal graphs.<ref>{{Cite book|url={{google books |plainurl=y |id=yWWEIvNgUQ4C|page=707}} |title=The Oxford Handbook of Causation |volume=1 |editor-last=Beebee |editor-first=Helen|editor-last2=Hitchcock|editor-first2=Christopher|editor-last3=Menzies|editor-first3=Peter|date=2012-01-12|publisher=OUP Oxford|isbn=9780191629464|language=en|first=Samir |last=Okasha |chapter=Causation in Biology|chapter-url=http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199279739.001.0001/oxfordhb-9780199279739-e-0036|doi=10.1093/oxfordhb/9780199279739.001.0001 }}</ref> He developed this approach while attempting to untangle the relative impacts of [[heredity]], development and environment on [[guinea pig]] coat patterns. He backed up his then-heretical claims by showing how such analyses could explain the relationship between guinea pig birth weight, ''[[Uterus|in utero]]'' time and litter size. Opposition to these ideas by prominent statisticians led them to be ignored for the following 40 years (except among animal breeders). Instead scientists relied on correlations, partly at the behest of Wright's critic (and leading statistician), [[Ronald Fisher|Fisher]].<ref name=":1" /> One exception was Burks, a student who in 1926 was the first to apply path diagrams to represent a mediating influence (''mediator'') and to assert that holding a mediator constant induces errors. She may have invented path diagrams independently.<ref name=":1" />
In 1921 [[Sewall Wright|Wright]]'s [[Path analysis (statistics)|path analysis]] became the theoretical ancestor of causal modeling and causal graphs.<ref>{{Cite book|url={{google books |plainurl=y |id=yWWEIvNgUQ4C|page=707}} |title=The Oxford Handbook of Causation |volume=1 |editor-last=Beebee |editor-first=Helen|editor-last2=Hitchcock|editor-first2=Christopher|editor-last3=Menzies|editor-first3=Peter|date=2012-01-12|publisher=OUP Oxford|isbn=9780191629464|language=en|first=Samir |last=Okasha |chapter=Causation in Biology|chapter-url=http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780199279739.001.0001/oxfordhb-9780199279739-e-0036|doi=10.1093/oxfordhb/9780199279739.001.0001 }}</ref> He developed this approach while attempting to untangle the relative impacts of [[heredity]], development and environment on [[guinea pig]] coat patterns. He backed up his then-heretical claims by showing how such analyses could explain the relationship between guinea pig birth weight, ''[[Uterus|in utero]]'' time and litter size. Opposition to these ideas by prominent statisticians led them to be ignored for the following 40 years (except among animal breeders). Instead scientists relied on correlations, partly at the behest of Wright's critic (and leading statistician), [[Ronald Fisher|Fisher]].<ref name=":1" /> One exception was Burks, a student who in 1926 was the first to apply path diagrams to represent a mediating influence (''mediator'') and to assert that holding a mediator constant induces errors. She may have invented path diagrams independently.<ref name=":1" />
:<math>P\left ( 牙线|牙膏，价格*2 \right ) </math>
:<math>P (floss |toothpaste, price*2)</math>
Columbia University operates the Causal Artificial Intelligence Lab which is attempting to connect causal modeling theory to [[artificial neural network]]s.<ref>{{Cite web|url=https://www.technologyreview.com/s/615189/what-ai-still-cant-do/|title=What AI still can't do|last=Bergstein|first=Brian|website=MIT Technology Review|language=en-US|access-date=2020-02-20}}</ref>
Columbia University operates the Causal Artificial Intelligence Lab which is attempting to connect causal modeling theory to [[artificial neural network]]s.<ref>{{Cite web|url=https://www.technologyreview.com/s/615189/what-ai-still-cant-do/|title=What AI still can't do|last=Bergstein|first=Brian|website=MIT Technology Review|language=en-US|access-date=2020-02-20}}</ref>
== Ladder of causation ==
== Ladder of causation ==