用因果效应分数量化数据行对查询结果的影响，提升数据库可解释性。
The Causal-Effect Score in Data Management
- 提出因果效应分数，衡量数据行对查询结果的因果影响。
- 适用于经典与概率数据库，支持多种查询场景。
- 适合关注数据溯源与可信分析的研究者和工程师。
因果效应（CE）是衡量变量对观测结果因果影响的数值指标。尽管在多个领域广泛应用，但将其作为数据管理中的归因分数以评估数据库查询中元组的因果强度，仍处于初步探索阶段。本文在经典数据库与概率数据库背景下，系统引入、泛化并深入研究了所谓的因果效应分数，为数据行对查询结果的影响提供可量化的解释能力。
原文摘要 · Abstract (English)
The Causal Effect (CE) is a numerical measure of causal influence of variables on observed results. Despite being widely used in many areas, only preliminary attempts have been made to use CE as an attribution score in data management, to measure the causal strength of tuples for query answering in databases. In this work, we introduce, generalize and investigate the so-called Causal-Effect Score in the context of classical and probabilistic databases.
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