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artificial_intelligence/judea_pearl/README.md
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> complex probability models, identified conditional independence > complex probability models, identified conditional independence
> relationships as the key organizing principle for uncertain knowledge, > relationships as the key organizing principle for uncertain knowledge,
> and described an efficient, distributed, exact inference algorithm. > and described an efficient, distributed, exact inference algorithm.
> <cite>[ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm)</cite> > -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma. [A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma.
> Introduces minimal-model semantics as a basis for causal discovery, > Introduces minimal-model semantics as a basis for causal discovery,
> and shows that causal directionality can be inferred from patterns > and shows that causal directionality can be inferred from patterns
> of correlations without resorting to temporal information. > of correlations without resorting to temporal information.
> <cite>[ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm)</cite> > -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked. [Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked.
> Introduces the theory of causal diagrams and its associated do-calculus; > Introduces the theory of causal diagrams and its associated do-calculus;
> the first (and still the only) mathematical method to enable a > the first (and still the only) mathematical method to enable a
> systematic removal of confounding bias in observations. > systematic removal of confounding bias in observations.
> <cite>[ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm)</cite> > -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) - [The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) -
> Describes a computational model that explains how humans generate, > Describes a computational model that explains how humans generate,
> evaluate and distinguish counterfactual statements so swiftly and > evaluate and distinguish counterfactual statements so swiftly and
> consistently. > consistently.
> <cite>[ACM Turing Award Short Annotated Bibliography](http://amturing.acm.org/bib/pearl_2658896.cfm)</cite> > -- <cite>[ACM Turing Award Short Annotated Bibliography][1]</cite>
[1]: http://amturing.acm.org/bib/pearl_2658896.cfm