Title: Improving Model Robustness Using Causal Knowledge Show Chinese title

Title: 利用因果知识提高模型的鲁棒性

Abstract: For decades, researchers in fields, such as the natural and social sciences, have been verifying causal relationships and investigating hypotheses that are now well-established or understood as truth. These causal mechanisms are properties of the natural world, and thus are invariant conditions regardless of the collection domain or environment. We show in this paper how prior knowledge in the form of a causal graph can be utilized to guide model selection, i.e., to identify from a set of trained networks the models that are the most robust and invariant to unseen domains. Our method incorporates prior knowledge (which can be incomplete) as a Structural Causal Model (SCM) and calculates a score based on the likelihood of the SCM given the target predictions of a candidate model and the provided input variables. We show on both publicly available and synthetic datasets that our method is able to identify more robust models in terms of generalizability to unseen out-of-distribution test examples and domains where covariates have shifted. Show Chinese abstract

Abstract: 数十年来，自然和社会科学等领域的研究人员一直在验证因果关系并研究现已确立或被视为真理的假设。 这些因果机制是自然界的一部分，因此无论数据采集的领域或环境如何，它们都是不变的条件。 本文展示了如何利用因果图形式的先验知识来指导模型选择，即从一组已训练的网络中识别出对未见域最稳健且不变的模型。 我们的方法将先验知识（可能是不完整的）作为结构因果模型（SCM），并基于候选模型的目标预测和提供的输入变量下SCM的可能性计算一个得分。 我们在公开可用的数据集和合成数据集上表明，我们的方法能够在泛化到未见过的分布外测试样本和协变量发生偏移的域方面识别出更稳健的模型。