Cyclic Causal Discovery from Continuous Equilibrium Data

TL;DR

This paper introduces a novel method for learning cyclic causal models from continuous equilibrium data, effectively handling feedback loops and nonlinearities, with applications to biochemical networks and cellular signaling.

Contribution

It presents a new approach that models interventions on activity levels, approximates nonlinear mechanisms with local linearizations, and reconstructs cellular signaling networks from flow cytometry data.

Findings

Identifies feedback loops in biochemical data.

Provides more accurate data modeling at similar complexity.

Works with continuous, nonlinear data without assuming linearity.

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.