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JMLR
2010
128views more  JMLR 2010»
12 years 10 months ago
Learning Causal Structure from Overlapping Variable Sets
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges correspo...
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G...
NIPS
2008
13 years 5 months ago
Integrating Locally Learned Causal Structures with Overlapping Variables
In many domains, data are distributed among datasets that share only some variables; other recorded variables may occur in only one dataset. While there are asymptotically correct...
Robert E. Tillman, David Danks, Clark Glymour
IJCAI
2001
13 years 5 months ago
Active Learning for Structure in Bayesian Networks
The task of causal structure discovery from empirical data is a fundamental problem in many areas. Experimental data is crucial for accomplishing this task. However, experiments a...
Simon Tong, Daphne Koller
JMLR
2010
134views more  JMLR 2010»
12 years 10 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
ECSQARU
2001
Springer
13 years 8 months ago
Caveats for Causal Reasoning with Equilibrium Models
In this paper we examine the ability to perform causal reasoning with equilibrium models. We explicate a postulate, which we term the Manipulation Postulate, that is required in o...
Denver Dash, Marek J. Druzdzel