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24 lines
1.4 KiB
24 lines
1.4 KiB
[Reverend Bayes on inference engines: A distributed hierarchical approach](http://ftp.cs.ucla.edu/pub/stat_ser/r30.pdf) -
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> The paper that began the probabilistic revolution in AI
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> by showing how several desirable properties of reasoning systems
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> can be obtained through sound probabilistic inference.
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> It introduced tree-structured networks as concise representations of
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> complex probability models, identified conditional independence
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> relationships as the key organizing principle for uncertain knowledge,
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> and described an efficient, distributed, exact inference algorithm.
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[A theory of inferred causation](http://ftp.cs.ucla.edu/pub/stat_ser/r156-reprint.pdf) - with Thomas S. Verma.
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> Introduces minimal-model semantics as a basis for causal discovery,
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> and shows that causal directionality can be inferred from patterns
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> of correlations without resorting to temporal information.
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[Causal diagrams for empirical research](http://ftp.cs.ucla.edu/pub/stat_ser/R218-B-L.pdf) - extended version linked.
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> Introduces the theory of causal diagrams and its associated do-calculus;
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> the first (and still the only) mathematical method to enable a
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> systematic removal of confounding bias in observations.
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[The algorithmization of counterfactuals](http://ftp.cs.ucla.edu/pub/stat_ser/r360.pdf) -
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> Describes a computational model that explains how humans generate,
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> evaluate and distinguish counterfactual statements so swiftly and
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> consistently.
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