Abstract
We present an algorithm for causal structure discovery suited in the presence of continuous variables. We test a version based on partial correlation that is able to recover the structure of a recursive linear equations model and compare it to the well-known PC algorithm on large networks. PC is generally outperformed in run time and number of structural errors. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Pellet, J. P., & Elisseeff, A. (2007). A partial correlation-based algorithm for causal structure discovery with continuous variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4723 LNCS, pp. 229–239). Springer Verlag. https://doi.org/10.1007/978-3-540-74825-0_21
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