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Machine learning models frequently experience performance drops underdistribution shifts. The underlying cause of such shifts may be multiplesimultaneous factors such as changes in data quality, differences in specificcovariate distributions, or changes in the relationship between label andfeatures. When a model does fail during deployment, attributing performancechange to these factors is critical for the model developer to identify theroot cause and take mitigating actions. In this work, we introduce the problemof attributing performance differences between environments to distributionshifts in the underlying data generating mechanisms. We formulate the problemas a cooperative game where the players are distributions. We define the valueof a set of distributions to be the change in model performance when only thisset of distributions has changed between environments, and derive an importanceweighting method for computing the value of an arbitrary set of distributions.The contribution of each distribution to the total performance change is thenquantified as its Shapley value. We demonstrate the correctness and utility ofour method on synthetic, semi-synthetic, and real-world case studies, showingits effectiveness in attributing performance changes to a wide range ofdistribution shifts.

"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts