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。

混合方块神经 ODE 因果建模

Hybrid Square Neural ODE Causal Modeling

Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D)
management strategies to be evaluated without patient harm. Deep learning
algorithms provide a promising avenue for extending simulator capabilities;
however, these algorithms are limited in that they do not necessarily learn
physiologically correct glucose dynamics and can learn incorrect and
potentially dangerous relationships from confounders in training data. This is
likely to be more important in real-world scenarios, as data is not collected
under strict research protocol. This work explores the implications of using
deep learning algorithms trained on real-world data to model glucose dynamics.
Free-living data was processed from the OpenAPS Data Commons and supplemented
with patient-reported tags of challenging diabetes events, constituting one of
the most detailed real-world T1D datasets. This dataset was used to train and
evaluate state-of-the-art glucose simulators, comparing their prediction error
across safety critical scenarios and assessing the physiological
appropriateness of the learned dynamics using Shapley Additive Explanations
(SHAP). While deep learning prediction accuracy surpassed the widely-used
mathematical simulator approach, the model deteriorated in safety critical
scenarios and struggled to leverage self-reported meal and exercise
information. SHAP value analysis also indicated the model had fundamentally
confused the roles of insulin and carbohydrates, which is one of the most basic
T1D management principles. This work highlights the importance of considering
physiological appropriateness when using deep learning to model real-world
systems in T1D and healthcare more broadly, and provides recommendations for
building models that are robust to real-world data constraints.

使用深度学习算法模拟血糖动力学的实际应用和局限性的研究。