Causal hybrid modeling with double machine learning
arxiv(2024)
摘要
Hybrid modeling integrates machine learning with scientific knowledge to
enhance interpretability, generalization, and adherence to natural laws.
Nevertheless, equifinality and regularization biases pose challenges in hybrid
modeling to achieve these purposes. This paper introduces a novel approach to
estimating hybrid models via a causal inference framework, specifically
employing Double Machine Learning (DML) to estimate causal effects. We showcase
its use for the Earth sciences on two problems related to carbon dioxide
fluxes. In the Q_10 model, we demonstrate that DML-based hybrid modeling is
superior in estimating causal parameters over end-to-end deep neural network
(DNN) approaches, proving efficiency, robustness to bias from regularization
methods, and circumventing equifinality. Our approach, applied to carbon flux
partitioning, exhibits flexibility in accommodating heterogeneous causal
effects. The study emphasizes the necessity of explicitly defining causal
graphs and relationships, advocating for this as a general best practice. We
encourage the continued exploration of causality in hybrid models for more
interpretable and trustworthy results in knowledge-guided machine learning.
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