Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in graphical models
An Improved Approach for the Discovery of Causal Models via MML
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A study of causal discovery with weak links and small samples
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Study of ensemble strategies in discovering linear causal models
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithm proposed by Wallace et al. 10. has demonstrated its ability in discovering Linear Causal Models from data. To explore the ways to improve efficiency, this research examines three different encoding schemes and four searching strategies. The experimental results reveal that (1) specifying parents encoding method is the best among three encoding methods we examined; (2) In the discovery of linear causal models, local Hill climbing works very well compared to other more sophisticated methods, like Markov Chain Monte Carto (MCMC), Genetic Algorithm (GA) and Parallel MCMC searching.