Multi-instance learning (MIL) deals with tasks where each example is
represented by a bag of instances. Unlike traditional supervised learning, only
the bag labels are observed whereas the label for each instance in the bags is
not available. Previous MIL studies typically assume that training and the test
data follow the same distribution, which is often violated in real-world
applications. Existing methods address distribution changes by reweighting the
training bags with the density ratio between the test and the training data.
However, models are frequently trained without prior knowledge of the testing
distribution which renders existing methods ineffective. In this paper, we
propose a novel multi-instance learning algorithm which links MIL with causal
inference to achieve stable prediction without knowing the distribution of the
test dataset. Experimental results show that the performance of our approach is
stable to the distribution changes.

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