papers-we-love_papers-we-love/artificial_intelligence/judea_pearl/README.md

[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.

[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.

[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.

[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.