papers-we-love_papers-we-love/artificial_intelligence/judea_pearl

Reverend Bayes on inference engines: A distributed hierarchical approach -

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 - 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 - 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 -

Describes a computational model that explains how humans generate, evaluate and distinguish counterfactual statements so swiftly and consistently.