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» Identifying Linear Causal Effects
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UAI
2008
13 years 9 months ago
On Identifying Total Effects in the Presence of Latent Variables and Selection bias
Assume that cause-effect relationships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model We consider the identi...
Manabu Kuroki, Zhihong Cai
FLAIRS
2010
13 years 9 months ago
Generalized Non-impeding Noisy-AND Trees
To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs assessed for each node. Its complexity is generally expon...
Yang Xiang
ICONIP
2007
13 years 9 months ago
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes
Abstract. An effective way to examine causality is to conduct an experiment with random assignment. However, in many cases it is impossible or too expensive to perform controlled ...
Shohei Shimizu, Aapo Hyvärinen
UAI
1994
13 years 8 months ago
A Decision-based View of Causality
Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intell...
David Heckerman, Ross D. Shachter
CORR
2012
Springer
185views Education» more  CORR 2012»
12 years 3 months ago
Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be iden...
Johannes Textor, Maciej Liskiewicz