  Multi-instance learning (MIL) deals with objects represented as bags of
instances and can predict instance labels from bag-level supervision. However,
significant performance gaps exist between instance-level MIL algorithms and
supervised learners since the instance labels are unavailable in MIL. Most
existing MIL algorithms tackle the problem by treating multi-instance bags as
harmful ambiguities and predicting instance labels by reducing the supervision
inexactness. This work studies MIL from a new perspective by considering bags
as auxiliary information, and utilize it to identify instance-level causal
representations from bag-level weak supervision. We propose the CausalMIL
algorithm, which not only excels at instance label prediction but also provides
robustness to distribution change by synergistically integrating MIL with
identifiable variational autoencoder. Our approach is based on a practical and
general assumption: the prior distribution over the instance latent
representations belongs to the non-factorized exponential family conditioning
on the multi-instance bags. Experiments on synthetic and real-world datasets
demonstrate that our approach significantly outperforms various baselines on
instance label prediction and out-of-distribution generalization tasks.
