CFS-MTL: A Causal Feature Selection Mechanism for Multi-task Learning via Pseudo-intervention

Multi-task learning (MTL) has been successfully applied to a wide range of real-world applications. However, MTL models often suffer from performance degradation with negative transfer due to sharing all features without distinguishing their helpfulness for all tasks. To this end, many works on feature selection for multi-task learning (FS-MTL) have been proposed to alleviate negative transfer between tasks by learning features selectively for each specific task. However, due to latent confounders between features and task targets, the correlations captured by the feature selection modules proposed in these works may fail to reflect the actual effect of the features on the targets. This paper explains negative transfer in FS-MTL from a causal perspective and presents a novel architecture called Causal Feature Selection for Multi-task Learning(CFS-MTL). This method incorporates the idea of causal inference into feature selection for multi-task learning via pseudo-intervention. It aims to select features with more stable causal effects rather than spurious correlations for each task by regularizing the distance between feature ITEs and feature importance. We conduct extensive experiments based on three real-world datasets to demonstrate that our proposed CFS-MTL outperforms state-of-the-art MTL models significantly in the AUC metric.