No $D_{\text{train}}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning
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无需训练数据：利用强化学习生成模型无关的反事实解释
- 研究问题
- 在无法访问模型训练数据时，如何为静态输入和多变量时间序列生成反事实解释，并减少改变模型决策所需的输入调整？
- 方法与思路
- 作者提出 NTD-CFE，将多变量时间序列的反事实搜索降至较低维空间，再以强化学习搜索，并允许用户指定特征限制和因果约束。
- 材料陈述的贡献
- 摘要称，该方法适用于连续与离散特征；在若干数据集上与四个基准比较时，所得时间序列反事实需要更少、更小的输入改变。
- 阅读关注点
- 应核对降维如何保留可行解释、奖励函数与模型查询要求，以及特征限制和因果约束如何落实，比较是否使用一致的搜索预算。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未说明具体约束和比较数值，输入改动较小也不能直接证明现实干预有效。
原文摘要
Machine learning (ML) methods have experienced significant growth in the past decade, yet their practical application in high-impact real-world domains has been hindered by their opacity. When ML methods are responsible for making critical decisions, stakeholders often require insights into how to alter these decisions. Counterfactual explanations (CFEs) have emerged as a solution, offering interpretations of opaque ML models and providing a pathway to transition from one decision to another. However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none of model-agnostic CFE methods can handle multivariate time-series without training datasets. These limitations can be formidable in many scenarios. In this paper, we present NTD-CFE, a novel model-agnostic CFE method based on reinforcement learning (RL) that generates CFEs when training datasets are unavailable. NTD-CFE is suitable for both static and multivariate time-series datasets with continuous and discrete features. NTD-CFE reduces the CFE search space from a multivariate time-series domain to a lower dimensional space and addresses the problem using RL. Users have the flexibility to specify non-actionable, immutable, and preferred features, as well as causal constraints. We demonstrate the performance of NTD-CFE against four baselines on several datasets and find that, despite not having access to a training dataset, NTD-CFE finds CFEs that make significantly fewer and significantly smaller changes to the input time-series. These properties make CFEs more actionable, as the magnitude of change required to alter an outcome is vastly reduced. The code is available in the supplementary material.