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» Identifying Linear Causal Effects
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JMLR
2010
134views more  JMLR 2010»
13 years 2 months ago
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation models with instantaneous effects. Estimation of ...
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Pa...
JMLR
2006
113views more  JMLR 2006»
13 years 7 months ago
Learning the Structure of Linear Latent Variable Models
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
CORR
2006
Springer
144views Education» more  CORR 2006»
13 years 7 months ago
Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
AAAI
2012
11 years 9 months ago
Transportability of Causal Effects: Completeness Results
The study of transportability aims to identify conditions under which causal information learned from experiments can be reused in a different environment where only passive obser...
Elias Bareinboim, Judea Pearl
BMCBI
2007
215views more  BMCBI 2007»
13 years 7 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer