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 that test the reproducibility of data with a specific mechanistic model to infer causality were developed. 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 monotonic ODE model to reproduce time-series data. We built a user-friendly computational package, General ODE-Based Inference (GOBI), which is applicable to nearly any monotonic system with positive and negative regulations described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both the 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.

为了识别因果关系，无模型推理方法（例如格兰杰因果关系）由于其灵活性而被广泛使用。然而，他们很难区分同步性和间接影响与直接因果关系，从而导致错误的预测。为了克服这个问题，开发了基于模型的推理方法，使用特定的机械模型测试数据的再现性以推断因果关系。然而，它们只能应用于特定模型描述的系统，极大地限制了它们的适用性。在这里，我们通过为一般单调 ODE 模型导出一个易于测试的条件来重现时间序列数据来解决这一限制。我们构建了一个用户友好的计算包，即基于 ODE 的通用推理 (GOBI)，它适用于几乎任何具有 ODE 描述的正负调节的单调系统。与现有的无模型方法不同，GOBI 在分子和群体水平上成功推断了各种网络中的正向和负向调节。因此，这种准确且广泛适用的推理方法是理解复杂动力系统的有力工具。