Cyclic Causal Discovery from Continuous Equilibrium Data

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.

Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)

References in corpus (3)

• On the Identifiability of the Post-Nonlinear Causal Model
• Identifiability of Causal Graphs using Functional Models
• Modeling Discrete Interventional Data using Directed Cyclic Graphical Models

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• Higher-order least squares: assessing partial goodness of fit of linear causal models
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• C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation
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