Performance of machine learning models may differ between training and deployment for many reasons. For instance, model performance can change between environments due to changes in data quality, observing a different population than the one in training, or changes in the relationship between labels and features. These changes result in distribution shifts across environments. Attributing model performance changes to specific shifts is critical for identifying sources of model failures, and for taking mitigating actions that ensure robust models. In this work, we introduce the problem of attributing performance differences between environments to distribution shifts in the underlying data generating mechanisms. We formulate the problem as a cooperative game where the players are distributions. We define the value of a set of distributions to be the change in model performance when only this set of distributions has changed between environments, and derive an importance weighting method for computing the value of an arbitrary set of distributions. The contribution of each distribution to the total performance change is then quantified as its Shapley value. We demonstrate the correctness and utility of our method on synthetic, semi-synthetic, and real-world case studies, showing its effectiveness in attributing performance changes to a wide range of distribution shifts.

翻译：机器学习模型在训练和部署环境中的性能可能因多种原因而异。例如，由于数据质量变化、观测到与训练时不同的总体、或标签与特征之间关系的变化，模型性能可能在不同环境中发生变化。这些变化导致跨环境的分布偏移。将模型性能变化归因于特定偏移对于识别模型失败的根源以及采取缓解措施以确保模型鲁棒性至关重要。在本工作中，我们提出将不同环境之间的性能差异归因于底层数据生成机制中的分布偏移问题。我们将该问题形式化为一个合作博弈，其中参与者为分布。我们定义一组分布的价值为：当只有这组分布在不同环境之间发生变化时，模型性能的变化量，并推导出一种重要性加权方法来计算任意一组分布的价值。每个分布对总性能变化的贡献则通过其沙普利值（Shapley value）量化。我们通过合成、半合成及真实世界案例研究展示了所提方法的正确性与实用性，证明了其在将性能变化归因于广泛分布偏移中的有效性。