Hybrid models combine mechanistic ODE-based dynamics with flexible and
expressive neural network components. Such models have grown rapidly in
popularity, especially in scientific domains where such ODE-based modeling
offers important interpretability and validated causal grounding (e.g., for
counterfactual reasoning). The incorporation of mechanistic models also
provides inductive bias in standard blackbox modeling approaches, critical when
learning from small datasets or partially observed, complex systems.
Unfortunately, as hybrid models become more flexible, the causal grounding
provided by the mechanistic model can quickly be lost. We address this problem
by leveraging another common source of domain knowledge: ranking of treatment
effects for a set of interventions, even if the precise treatment effect is
unknown. We encode this information in a causal loss that we combine with the
standard predictive loss to arrive at a hybrid loss that biases our learning
towards causally valid hybrid models. We demonstrate our ability to achieve a
win-win -- state-of-the-art predictive performance and causal validity -- in
the challenging task of modeling glucose dynamics during exercise.

通过将机械化 ODE 动力学与灵活且富有表现力的神经网络组件结合起来，我们解决了混合模型中因机械化模型的灵活性而失去因果关系的问题，并在建模运动期间的血糖动力学方面实现了既具有领先预测性能又具有因果有效性的双赢。