= Richard Scheines
{c}
<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>.<Richard Scheines by Carnegie Mellon University>[Bio, first two paragraphs: causal discovery, statistical evidence and Tetrad collaboration]{ref}
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.<Causation, Prediction, and Search by Peter Spirtes, Clark Glymour and Richard Scheines>[Chapter 3, Markov and faithfulness assumptions; chapter 5, discovery algorithms]{ref}
\Image[https://www.cmu.edu/dietrich/philosophy/images/800x800/scheines-richard.jpg]
{title=Portrait of <Richard Scheines>}
{description=Portrait selected from the matching Carnegie Mellon institutional profile.}
{source=https://www.cmu.edu/dietrich/philosophy/tetrad/about/people/scheines.html}
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