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📜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.