Atlas home || Conferences | Abstracts | about Atlas
Host: Fields Institute
Homepage: http://www.fields.utoronto.ca/cissem3.html
Email: cissem3@fields.utoronto.ca
Organizers: David Heckerman (Microsoft Research), Steffen Lauritzen (Aalborg University)
Description:
Seminar 1 and 2 introduce connections between causal interpretations of graphs and their conditional independence properties.
This seminar will discuss how these connections can be applied to the problem of learning about causal relations from data.
We consider both Bayesian and asymptotic approaches, with an emphasis on the former. We relate causal interpretations to commonly used assumptions used for the selection of graph structure such as parameter independence, parameter modularity, and marginal likelihood equivalence. In addition, we address difficulties in scoring and searching over graphical models with latent variables, compare model selection to model averaging techniques, and discuss assumptions under which "counterfactual" information can be learned.
Speakers: D.M. Chickering (Microsoft Research), T. Richardson (University of Warwick), G. Cooper (University of Pittsburgh), J. Robins (Harvard School of Public Health), D. Geiger (Technion), D. Rubin (Harvard University), P. Giudici (University of Pavia), R. Scheines (Carnegie Mellon University), C. Glymour (Carnegie Mellon University), R. Shachter (Stanford University), M. Goldszmidt (SRI International), G. Shafer (Rutgers University), D. Madigan (University of Washington), P. Spirtes (Carnegie Mellon University), C. Meek (Microsoft Research), J. Whittaker (Lancaster University), J. Pearl (University of California Los Angeles)
Date received: September 30, 1999
© 2008 Atlas Conferences Inc.