research
∙
09/26/2013
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure
We present a very general approach to learning the structure of causal m...
share
research
∙
10/16/2012
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Much of scientific data is collected as randomized experiments interveni...
research
∙
07/09/2012
Estimating a Causal Order among Groups of Variables in Linear Models
The machine learning community has recently devoted much attention to th...
research
∙
07/04/2012
Discovery of non-gaussian linear causal models using ICA
In recent years, several methods have been proposed for the discovery of...
research
∙
06/13/2012
Discovering Cyclic Causal Models by Independent Components Analysis
We generalize Shimizu et al's (2006) ICA-based approach for discovering ...
research
∙
06/13/2012
Causal discovery of linear acyclic models with arbitrary distributions
An important task in data analysis is the discovery of causal relationsh...
research
∙
05/09/2012
Bayesian Discovery of Linear Acyclic Causal Models
Methods for automated discovery of causal relationships from non-interve...
research
∙
02/14/2012
Noisy-OR Models with Latent Confounding
Given a set of experiments in which varying subsets of observed variable...
research
∙
01/13/2011
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Structural equation models and Bayesian networks have been widely used t...
research
∙
09/24/2009
Telling cause from effect based on high-dimensional observations
We describe a method for inferring linear causal relations among multi-d...
research
∙
08/25/2004
Non-negative matrix factorization with sparseness constraints
Non-negative matrix factorization (NMF) is a recently developed techniqu...
research
∙
02/11/2002