论文标题
在高维度中深入学习因果结构
Deep Learning of Causal Structures in High Dimensions
论文作者
论文摘要
近年来，因果关系与机器学习之间的交集取得了迅速的进步。由涉及高维数据的科学应用，尤其是在生物医学中，我们提出了一种深层神经架构，用于从经验数据和先前的因果知识的结合中学习变量之间的因果关系。我们将卷积和图形神经网络结合在因果风险框架中，以提供灵活且可扩展的方法。经验结果包括线性和非线性模拟（其中已知的基本因果结构并可以直接比较），以及一个真实的生物学示例，其中模型适用于高维分子数据及其输出与完全看不见的验证实验相比。这些结果表明，使用深度学习方法在跨越数千个变量的大规模问题中学习因果网络的可行性。
Recent years have seen rapid progress at the intersection between causality and machine learning. Motivated by scientific applications involving high-dimensional data, in particular in biomedicine, we propose a deep neural architecture for learning causal relationships between variables from a combination of empirical data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide a flexible and scalable approach. Empirical results include linear and nonlinear simulations (where the underlying causal structures are known and can be directly compared against), as well as a real biological example where the models are applied to high-dimensional molecular data and their output compared against entirely unseen validation experiments. These results demonstrate the feasibility of using deep learning approaches to learn causal networks in large-scale problems spanning thousands of variables.
