Uncertainty in Artificial Intelligence
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Cyclic Causal Discovery from Continuous Equilibrium Data
Joris Mooij, Tom Heskes
Abstract:
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability to work with continuous data (without assuming linearity) and to deal with feedback loops. Within the context of biochemical reactions, we also propose a novel way of modeling interventions that modify the activity of compounds instead of their abundance. For computational reasons, we approximate the nonlinear causal mechanisms by (coupled) local linearizations, one for each experimental condition. We apply the method to reconstruct a cellular signaling network from the flow cytometry data measured by Sachs et al. (2005). We show that our method finds evidence in the data for feedback loops and that it gives a more accurate quantitative description of the data at comparable model complexity.
Keywords:
Pages: 431-439
PS Link:
PDF Link: /papers/13/p431-mooij.pdf
BibTex:
@INPROCEEDINGS{Mooij13,
AUTHOR = "Joris Mooij and Tom Heskes",
TITLE = "Cyclic Causal Discovery from Continuous Equilibrium Data",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "431--439"
}


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