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 [Analysis of Three Bayesian Network Inference Algorithms:Variable Elimination, Likelihood Weighting, and Gibbs Sampling](https://github.com/papers-we-love/papers-we-love/blob/master/artificial_intelligence/3-bayesian-network-inference-algorithm.pdf) by Rose F. Liu, Rusmin Soetjipto
 
 [Computing Machinery and Intelligence](http://www.csee.umbc.edu/courses/471/papers/turing.pdf) by A.M. Turing
+
+[Judea Pearl](http://bayes.cs.ucla.edu/jp_home.html) folder - Papers by Judea Pearl, 2011 winner of the ACM Turing Award.
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+[Reverend Bayes on inference engines: A distributed hierarchical approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) -
+> The paper that began the probabilistic revolution in AI
+> by showing how several desirable properties of reasoning systems
+> can be obtained through sound probabilistic inference.
+> It introduced tree-structured networks as concise representations of
+> 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