We propose a simple and scalable approach to causal representation learning for multitask learning. Our approach requires minimal modification to existing ML systems, and improves robustness to prior probability shift. The improvement comes from mitigating unobserved confounders that cause the targets, but not the input. We refer to them as target-causing confounders. These confounders induce spurious dependencies between the input and targets. This poses a problem for the conventional approach to multitask learning, due to its assumption that the targets are conditionally independent given the input. Our proposed approach takes into account the dependency between the targets in order to alleviate target-causing confounding. All that is required in addition to usual practice is to estimate the joint distribution of the targets to switch from discriminative to generative classification, and to predict all targets jointly. Our results on the Attributes of People and Taskonomy datasets reflect the conceptual improvement in robustness to prior probability shift.

提出了一种简单，可扩展的方法生成多任务学习(GMTL)，用于多任务学习的因果表示学习。该方法改变了传统的多任务推理目标，并增强了对目标转移的鲁棒性，它可以与现有的多任务学习方法一起使用而无需额外的训练。我们的结果表明，应用GMTL可以提高属性与Taskonomy数据集对于四种不同多任务学习方法的鲁棒性。

生成式多任务学习缓解目标造成的混淆