论文标题
分配歧义的反事实计划
Counterfactual Plans under Distributional Ambiguity
论文作者
论文摘要
由于机器学习模型在结果域中的繁荣应用，反事实解释引起了极大的关注。反事实计划由修改给定实例的多种可能性组成，以便将更改模型的预测。由于可以根据新数据的未来到达预测模型，因此相反的计划在模型参数的未来值方面可能变得无效或不可行。在这项工作中，我们研究了模型不确定性下的反事实计划，其中仅使用第一阶和第二矩信息来部分规定模型参数的分布。首先，我们提出了一个不确定性量化工具，以计算任何给定的反事实计划的有效性概率的下限和上限。然后，我们提供纠正方法来调整反事实计划以提高有效性度量。数值实验验证了我们的界限，并证明我们的校正会增加不同现实世界数据集中反事实计划的鲁棒性。
Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains. A counterfactual plan consists of multiple possibilities to modify a given instance so that the model's prediction will be altered. As the predictive model can be updated subject to the future arrival of new data, a counterfactual plan may become ineffective or infeasible with respect to the future values of the model parameters. In this work, we study the counterfactual plans under model uncertainty, in which the distribution of the model parameters is partially prescribed using only the first- and second-moment information. First, we propose an uncertainty quantification tool to compute the lower and upper bounds of the probability of validity for any given counterfactual plan. We then provide corrective methods to adjust the counterfactual plan to improve the validity measure. The numerical experiments validate our bounds and demonstrate that our correction increases the robustness of the counterfactual plans in different real-world datasets.
