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Host: Fields Institute
Homepage: http://www.fields.utoronto.ca/cissem1.html
Email: cissem1@fields.utoronto.ca.
Organizers: Phil Dawid (University College London), Glenn Shafer (Rutgers University)
Description:
Graphical models have been traditionally seen as ways of describing and manipulating probabilistic conditional independence.
However, they are often given informal causal interpretations, and there now exist mathematical ways of making these precise
and manipulating them.
This research seminar will aim to extend understanding of these methods, and to explore the scope of graphical modelling as a tool for causal inference and analysis. Attention will also focus on the nature of the relationship between causal interpretations and conditional independence properties of graphical representations, the two main themes of the overall program.
Speakers: M. Eerola (Rolf Nevanlinna Institute), J. Robins (Harvard School of Public Health), M. Forster (University of Wisconsin), D. Rubin (Harvard University), P. Holland (University of California), R. Scheines (Carnegie Mellon University), S. Lauritzen (Aalborg University), R. Shachter (Stanford University), W. Oldford (University of Waterloo), M. Studeny (Academy of Science, Czech Republic), J. Pearl (University of California), L. Wasserman (Carnegie Mellon University)
Date received: September 30, 1999
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