因果决策中反事实假设的分解
Decomposing Counterfactual Explanations for Consequential Decision Making
摘要：
算法追索的目标是扭转不利的决定（例如，从贷款拒绝到批准）通过建议可操作的特征改变（例如，减少信用卡的数量）。为了生成低成本的追索权，大多数方法都是在假设特征是可独立操作的（IMF）的情况下工作的。为了解决特征依赖性问题，通常通过因果追索范式来研究追索问题。然而，众所周知，强假设，编码在因果模型和结构方程，阻碍了这些方法的适用性在复杂的领域，因果依赖结构是模糊的。在这项工作中，我们开发了\texttt{DEAR}（解开因果关系追索权），一个新颖而实用的追索框架，弥补了国际货币基金组织和强因果假设之间的差距。\texttt{DEAR}通过从有希望的追索权特征的子集中分离出共变特征的潜在表示来生成追索权，以捕获主要的实际追索权需求。我们对真实世界数据的实验证实了我们的理论动机的追索权模型，并强调了我们的框架的能力，提供可靠的，低成本的追索权存在的功能依赖。
英文摘要：
The goal of algorithmic recourse is to reverse unfavorable decisions (e.g., from loan denial to approval) under automated decision making by suggesting actionable feature changes (e.g., reduce the number of credit cards). To generate low-cost recourse the majority of methods work under the assumption that the features are independently manipulable (IMF). To address the feature dependency issue the recourse problem is usually studied through the causal recourse paradigm. However, it is well known that strong assumptions, as encoded in causal models and structural equations, hinder the applicability of these methods in complex domains where causal dependency structures are ambiguous. In this work, we develop \texttt{DEAR} (DisEntangling Algorithmic Recourse), a novel and practical recourse framework that bridges the gap between the IMF and the strong causal assumptions. \texttt{DEAR} generates recourses by disentangling the latent representation of co-varying features from a subset of promising recourse features to capture the main practical recourse desiderata. Our experiments on real-world data corroborate our theoretically motivated recourse model and highlight our framework's ability to provide reliable, low-cost recourse in the presence of feature dependencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
