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Multi-instance learning (MIL) deals with objects represented as bags ofinstances and can predict instance labels from bag-level supervision. However,significant performance gaps exist between instance-level MIL algorithms andsupervised learners since the instance labels are unavailable in MIL. Mostexisting MIL algorithms tackle the problem by treating multi-instance bags asharmful ambiguities and predicting instance labels by reducing the supervisioninexactness. This work studies MIL from a new perspective by considering bagsas auxiliary information, and utilize it to identify instance-level causalrepresentations from bag-level weak supervision. We propose the CausalMILalgorithm, which not only excels at instance label prediction but also providesrobustness to distribution change by synergistically integrating MIL withidentifiable variational autoencoder. Our approach is based on a practical andgeneral assumption: the prior distribution over the instance latentrepresentations belongs to the non-factorized exponential family conditioningon the multi-instance bags. Experiments on synthetic and real-world datasetsdemonstrate that our approach significantly outperforms various baselines oninstance label prediction and out-of-distribution generalization tasks.

Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization