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.
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Pellet, JP., Elisseeff, A. (2007). A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables. In: R. Berthold, M., Shawe-Taylor, J., Lavrač, N. (eds) Advances in Intelligent Data Analysis VII. IDA 2007. Lecture Notes in Computer Science, vol 4723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74825-0_21
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DOI: https://doi.org/10.1007/978-3-540-74825-0_21
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