A general model-based causal inference overcomes the curse of synchrony and indirect effect

Abstract

To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect effects from direct causation, leading to false predictions. To overcome this, model-based inference methods were developed that test the reproducibility of data with a specific mechanistic model to infer causality. However, they can only be applied to systems described by a specific model, greatly limiting their applicability. Here, we address this limitation by deriving an easily-testable condition for a general ODE model to reproduce time-series data. We built a user-friendly computational package, GOBI (General ODE-Based Inference), which is applicable to nearly any system described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both molecular and population levels, unlike existing model-free methods. Thus, this accurate and broadly-applicable inference method is a powerful tool for understanding complex dynamical systems.