论文标题
解释单位级别更改的根本原因
Explaining the root causes of unit-level changes
论文作者
论文摘要
可解释的AI和可解释的ML的现有方法无法解释统计单元的输出变量值的变化，以输入值的变化以及“机制”的变化（函数转换为输出的函数）。我们提出了两种基于反事实的方法，用于使用游戏理论中的沙普利值的概念来解释各种输入粒度的单位级变化。这些方法满足了对于任何单位级别更改归因方法所需的两个关键公理。通过模拟，我们研究了所提出方法的可靠性和可扩展性。我们从一个案例研究中获得了明智的结果，该案例研究确定了美国个人收入变化的驱动因素。
Existing methods of explainable AI and interpretable ML cannot explain change in the values of an output variable for a statistical unit in terms of the change in the input values and the change in the "mechanism" (the function transforming input to output). We propose two methods based on counterfactuals for explaining unit-level changes at various input granularities using the concept of Shapley values from game theory. These methods satisfy two key axioms desirable for any unit-level change attribution method. Through simulations, we study the reliability and the scalability of the proposed methods. We get sensible results from a case study on identifying the drivers of the change in the earnings for individuals in the US.
