<ul><li>Researchers have developed a methodology called causality-informed neural network (CINN) to improve the predictive performance of neural networks.</li><li>CINN leverages three steps to encode hierarchical causality structure into the neural network, discovered through causal discovery from observational data.</li><li>The discovered causal relationships are systematically encoded into the neural network's architecture and loss function, preserving the relative order and co-learning of different types of nodes.</li><li>Computational experiments show that CINN outperforms other state-of-the-art methods in predictive performance, highlighting the value of integrating causal knowledge.</li></ul>

Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge

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