Cyclic Causal Discovery from Continuous Equilibrium Data
This work addresses causal discovery in biochemical networks with feedback loops, but it is incremental as it builds on existing methods with approximations like local linearizations.
The paper tackles the problem of learning cyclic causal models from observational and interventional equilibrium data, achieving a more accurate quantitative description of flow cytometry data at comparable model complexity.
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.