  When applying multi-instance learning (MIL) to make predictions for bags of
instances, the prediction accuracy of an instance often depends on not only the
instance itself but also its context in the corresponding bag. From the
viewpoint of causal inference, such bag contextual prior works as a confounder
and may result in model robustness and interpretability issues. Focusing on
this problem, we propose a novel interventional multi-instance learning (IMIL)
framework to achieve deconfounded instance-level prediction. Unlike traditional
likelihood-based strategies, we design an Expectation-Maximization (EM)
algorithm based on causal intervention, providing a robust instance selection
in the training phase and suppressing the bias caused by the bag contextual
prior. Experiments on pathological image analysis demonstrate that our IMIL
method substantially reduces false positives and outperforms state-of-the-art
MIL methods.
