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JMLR
2010
134views more  JMLR 2010»
13 years 3 days ago
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Jan Lemeire, Kris Steenhaut
JMLR
2010
93views more  JMLR 2010»
13 years 3 days ago
Sufficient covariates and linear propensity analysis
Working within the decision-theoretic framework for causal inference, we study the properties of "sufficient covariates", which support causal inference from observation...
Hui Guo, A. Philip Dawid
JMLR
2010
143views more  JMLR 2010»
13 years 3 days ago
Beware of the DAG!
Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
A. Philip Dawid
UAI
1994
13 years 6 months ago
A New Look at Causal Independence
Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction whe...
David Heckerman, John S. Breese

Publication
253views
13 years 3 months ago
Multisensory Oddity Detection as Bayesian Inference
A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possi...
Timothy Hospedales and Sethu Vijayakumar