提出可行动的因果解释，定位概念漂移的真正原因。
Causal Explanation of Concept Drift -- A Truly Actionable Approach
- 用因果推理替代传统方法，找出漂移的真实影响因素。
- 在多个实际场景中验证，能精准识别受漂移影响的关键特征。
- 适合需要针对性干预的工业与关键基础设施系统。
在不断变化的世界中，理解变化如何影响工业制造或关键基础设施等系统至关重要。解释机器学习中的概念漂移是实现针对性干预、避免模型失效及物理世界故障的第一步。本文将基于模型的漂移解释扩展为因果解释，显著提升解释的可行动性。我们在多个应用场景中评估该策略，结果表明，该框架能够准确隔离受概念漂移影响的因果相关特征，从而支持有针对性的干预措施。
原文摘要 · Abstract (English)
In a world that constantly changes, it is crucial to understand how those changes impact different systems, such as industrial manufacturing or critical infrastructure. Explaining critical changes, referred to as concept drift in the field of machine learning, is the first step towards enabling targeted interventions to avoid or correct model failures, as well as malfunctions and errors in the physical world. Therefore, in this work, we extend model-based drift explanations towards causal explanations, which increases the actionability of the provided explanations. We evaluate our explanation strategy on a number of use cases, demonstrating the practical usefulness of our framework, which isolates the causally relevant features impacted by concept drift and, thus, allows for targeted intervention.
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