Hybrid Square Neural ODE Causal Modeling

arXiv:2402.17233v1 Announce Type: new
Abstract: 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 …

abstract arxiv bias components counterfactual cs.lg domains dynamics hybrid inductive interpretability modeling network neural network reasoning square stat.ap stat.me type