= Thomas S. Richardson
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<Thomas S. Richardson> develops graphical approaches to <causal inference> and <statistical models>, including ways to reason when some variables are hidden. His institutional research description lists graphical models, model selection, Bayesian inference and causal models.https://stat.uw.edu/people/thomas-s-richardson[Research interests]{ref}
He is a coauthor of <Nested Markov Properties for Acyclic Directed Mixed Graphs by Thomas S. Richardson and others>. The work represents observable constraints of hidden-variable directed graphs using a <nested Markov model>. Its motivation is that eliminating a hidden cause can leave more than ordinary <conditional independence> restrictions among the measured variables.<Nested Markov Properties for Acyclic Directed Mixed Graphs by Thomas S. Richardson and others>[Introduction and characterization of marginal hidden-variable models]{ref}
For example, a hidden common cause of two observed variables can be represented by a bidirected edge in a <latent projection>. The graph records a possible dependence source without pretending that the common cause itself was measured. Such representations separate what observations constrain from what remains unidentifiable.
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