Robin J. Evans studies causal inference, graphical models and the behavior of statistical models after hidden variables are marginalized. His work on margins of Bayesian networks characterizes algebraic restrictions that are not captured by a list of conditional independences alone.[ref]
With Thomas S. Richardson and other collaborators, he coauthored Nested Markov Properties for Acyclic Directed Mixed Graphs by Thomas S. Richardson and others. A central idea is to describe constraints on the observed distribution without committing to an identifiable parameter for every unobserved mechanism.[ref]
This matters when two models agree on ordinary independence tests yet differ in other restrictions on probabilities. Separating these cases can improve both model interpretation and estimation: a convenient parameterization of the observed model need not identify the underlying hidden causal story. His institutional personal-page redirect leads to his research-group site.[ref]
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