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» The Structure of First-Order Causality
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JMLR
2010
134views more  JMLR 2010»
13 years 2 months 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
IJCAI
1989
13 years 8 months ago
Reasoning About Hidden Mechanisms
1 describe an approach to the problem of forming hypotheses about hidden mechanisms w; thin devices — the "black box" problem for physical systems. The approach involv...
Richard J. Doyle
IJAR
2008
155views more  IJAR 2008»
13 years 7 months ago
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
The task of estimating causal effects from non-experimental data is notoriously difficult and unreliable. Nevertheless, precisely such estimates are commonly required in many fiel...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen...
ECSQARU
2001
Springer
14 years 1 days ago
Supporting Changes in Structure in Causal Model Construction
The term “changes in structure,” originating from work in econometrics, refers to structural modifications invoked by actions on a causal model. In this paper we formalize the...
Tsai-Ching Lu, Marek J. Druzdzel
JMLR
2010
149views more  JMLR 2010»
13 years 2 months ago
Fast Committee-Based Structure Learning
Current methods for causal structure learning tend to be computationally intensive or intractable for large datasets. Some recent approaches have speeded up the process by first m...
Ernest Mwebaze, John A. Quinn