用因果规则生成可实现的反事实解释，让决策变通更可信
Generating Causally Compliant Counterfactual Explanations using ASP
- 基于因果规则建模特征间依赖，确保反事实路径合理
- 生成从负面结果到正面结果的可行改变路径
- 适合需要可解释、可操作决策建议的场景
本研究聚焦于生成可实现的反事实解释。针对机器学习模型或决策系统输出的负面结果，新方法CoGS生成（i）代表正向结果的反事实解，以及（ii）从负面结果通往正向结果的路径，路径中每个节点对应一个特征值的改变。CoGS所计算的路径严格遵守特征间的因果约束，因此生成的反事实具有现实可行性。CoGS采用基于规则的机器学习算法来建模特征间的因果依赖关系。论文讨论了当前研究进展与初步实验结果。
原文摘要 · Abstract (English)
This research is focused on generating achievable counterfactual explanations. Given a negative outcome computed by a machine learning model or a decision system, the novel CoGS approach generates (i) a counterfactual solution that represents a positive outcome and (ii) a path that will take us from the negative outcome to the positive one, where each node in the path represents a change in an attribute (feature) value. CoGS computes paths that respect the causal constraints among features. Thus, the counterfactuals computed by CoGS are realistic. CoGS utilizes rule-based machine learning algorithms to model causal dependencies between features. The paper discusses the current status of the research and the preliminary results obtained.
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