Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.

翻译：最近的工作使人们对统计机器学习模型中存在虚假关联和意外偏差的风险表示关切,这些错误和意外偏差威胁到模型的稳健性和公平性。在本文件中,我们建议采用一种简单和直观的正规化方法,在模型培训期间整合因果知识,并通过强调因果特征和不强调虚假特征来构建一个稳健和公平的模型。具体地说,我们首先手工确定因果和虚假特征,其原则源于因果推断的反事实框架。然后,我们提出一种正规化方法,分别惩罚因果和虚假特征。通过调整每种特征的处罚力度,我们建立了一个预测模型,更多地依赖因果特征,而较少依赖非因果特征。我们进行了实验,以评价三个数据集的模型稳健性和公平性,并使用多种指标。经验显示,以因果意识建立的新模型在反事实文本和敏感属性的模型公平性方面大大改进了模型的稳健性。