基于可解释性的Shapley信用分配
On Shapley Credit Allocation for Interpretability
摘要：
我们强调在解释学习模型的决策时提出正确问题的重要性。我们讨论了自然延伸的理论机器从Janzing等。2020年，它回答了“为什么我的模型预测一个人患有癌症？“回答了一个更复杂的问题，“是什么导致我的模型预测一个人患有癌症？”虽然前者量化了变量对模型的直接影响，但后者也考虑了间接影响，从而在人类可以根据因果关系进行推理的地方提供了有意义的见解。我们提出了三大类解释：观察，模型特定的因果关系，其中每一个都是重要的，在自己的权利。此外，本文通过将不同性质的解释与不同的度量交织在一起作为Shapley对称化的特征函数来量化特征相关性。除了广泛使用的模型的期望值，我们还讨论了统计不确定性和离散度作为信息候选人的措施，以及它们在为每个数据点生成解释时的优点，其中一些是第一次在这种情况下使用的。这些措施不仅是有用的研究变量对模型输出的影响，而且对模型的预测性能，为此，我们提出了相关的特征函数，也是第一次使用。
英文摘要：
We emphasize the importance of asking the right question when interpreting the decisions of a learning model. We discuss a natural extension of the theoretical machinery from Janzing et. al. 2020, which answers the question "Why did my model predict a person has cancer?" for answering a more involved question, "What caused my model to predict a person has cancer?" While the former quantifies the direct effects of variables on the model, the latter also accounts for indirect effects, thereby providing meaningful insights wherever human beings can reason in terms of cause and effect. We propose three broad categories for interpretations: observational, model-specific and causal each of which are significant in their own right. Furthermore, this paper quantifies feature relevance by weaving different natures of interpretations together with different measures as characteristic functions for Shapley symmetrization. Besides the widely used expected value of the model, we also discuss measures of statistical uncertainty and dispersion as informative candidates, and their merits in generating explanations for each data point, some of which are used in this context for the first time. These measures are not only useful for studying the influence of variables on the model output, but also on the predictive performance of the model, and for that we propose relevant characteristic functions that are also used for the first time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
