  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.
