Richard Scheines studies causal discovery: learning causal structure from statistical evidence under explicit assumptions. With Peter Spirtes, Clark Glymour and collaborators, he developed the Tetrad research programme, connecting causal inference, algorithms and machine learning.[ref]
Richard Scheines by Carnegie Mellon University. Bio, first two paragraphs: causal discovery, statistical evidence and Tetrad collaboration
In Causation, Prediction, and Search by Peter Spirtes, Clark Glymour and Richard Scheines, the collaborators examine how patterns of conditional independence constrain a causal directed acyclic graph. The connection is informative but conditional: faithfulness of a directed acyclic graph, restrictions on unobserved causes and the correctness of the independence information affect what can be identified. Different graphs can remain indistinguishable under Markov equivalence of directed acyclic graphs, so observational data need not select one unique causal story.[ref]
Causation, Prediction, and Search by Peter Spirtes, Clark Glymour and Richard Scheines. Chapter 3, Markov and faithfulness assumptions; chapter 5, discovery algorithms
Portrait of Richard Scheines
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