Generative multitask learning mitigates target-causing confounding

We propose generative multitask learning (GMTL), a simple and scalable approach to causal machine learning in the multitask setting. Our approach makes a minor change to the conventional multitask inference objective, and improves robustness to target shift. Since GMTL only modifies the inference objective, it can be used with existing multitask learning methods without requiring additional training. The improvement in robustness comes from mitigating unobserved confounders that cause the targets, but not the input. We refer to them as \emph{target-causing confounders}. These confounders induce spurious dependencies between the input and targets. This poses a problem for conventional multitask learning, due to its assumption that the targets are conditionally independent given the input.