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artificial_intelligence/judea_pearl/README.md

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