一个结合因果性、稀疏性和密度的可行反事实探索框架
A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density
AI总结：
本研究提出一个结合因果性、稀疏性和密度的反事实探索框架，利用黑盒分类器和变分自编码器生成满足因果约束的可行且稀疏的CF示例，并通过二维流形有效区分可行与不可行示例。
摘要：
通过输入的小扰动来解释机器学习模型输出的反事实（CF）解释的迫切需求，在研究界已备受关注。尽管CF示例的多样性很重要，但它们同时具备可行性的方面并不必然完全适用。本研究使用不同的基准数据集，通过保留其属性的逻辑因果关系来考察：在对原始输入进行少量更改后生成的CF示例，是否能够具备可行性，并在真实世界场景中真正对最终用户有用。为实现这一目标，我们使用黑盒模型作为分类器，将目标类与输入类区分开来，并使用变分自编码器（VAE）生成可行的CF示例。作为扩展，我们还提取了二维流形（每个数据集一个），这些流形定位了大多数可行示例，这种表示充分将它们与不可行示例区分开来。在我们的实验中，我们使用了三个常用数据集，并成功生成了既可行又稀疏的CF示例，这些示例满足所有可能的预定义因果约束，同时通过确认它们与数据集中属性的重要性来验证其有效性。
英文摘要：
The imminent need to interpret the output of a Machine Learning model with counterfactual (CF) explanations - via small perturbations to the input - has been notable in the research community. Although the variety of CF examples is important, the aspect of them being feasible at the same time, does not necessarily apply in their entirety. This work uses different benchmark datasets to examine through the preservation of the logical causal relations of their attributes, whether CF examples can be generated after a small amount of changes to the original input, be feasible and actually useful to the end-user in a real-world case. To achieve this, we used a black box model as a classifier, to distinguish the desired from the input class and a Variational Autoencoder (VAE) to generate feasible CF examples. As an extension, we also extracted two-dimensional manifolds (one for each dataset) that located the majority of the feasible examples, a representation that adequately distinguished them from infeasible ones. For our experimentation we used three commonly used datasets and we managed to generate feasible and at the same time sparse, CF examples that satisfy all possible predefined causal constraints, by confirming their importance with the attributes in a dataset.
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