摘要翻译：
在本文中，我们提出了因果关系作为解释查询答案和非答案的统一框架，从而推广和扩展了以前提出的几种出处和缺失查询结果解释方法。我们从Halpern和Pearl对实际原因的充分研究的定义开始发展我们的框架。在确定了原定义的一些不可取的特征后，我们提出了功能原因作为因果关系的一个精炼定义，具有几个可取的性质。这些性质使我们能够在数据库上下文中应用因果关系的概念，并以多种方式统一地应用它来定义查询结果的原因及其各自的贡献：(i)我们可以对出处和非答案进行建模，(ii)我们可以将解释定义为输入关系中的数据或查询计划中的关系操作，以及(iii)我们可以对单个原因进行分级责任，从而允许我们对原因进行排序。特别是，我们的方法允许我们解释对关系聚合函数的贡献，并根据它们各自的责任对原因进行排序。我们给出了复杂性的结果，并描述了在可处理的情况下评估因果关系的多项式算法。在整个论文中，我们用几个例子来说明我们的框架的适用性。总之，我们在本文中发展了数据库上下文中因果关系理论的理论基础。
---
英文标题：
《Why so? or Why no? Functional Causality for Explaining Query Answers》
---
作者：
Alexandra Meliou, Wolfgang Gatterbauer, Katherine F. Moore, Dan Suciu
---
最新提交年份：
2009
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
--
一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  In this paper, we propose causality as a unified framework to explain query answers and non-answers, thus generalizing and extending several previously proposed approaches of provenance and missing query result explanations.   We develop our framework starting from the well-studied definition of actual causes by Halpern and Pearl. After identifying some undesirable characteristics of the original definition, we propose functional causes as a refined definition of causality with several desirable properties. These properties allow us to apply our notion of causality in a database context and apply it uniformly to define the causes of query results and their individual contributions in several ways: (i) we can model both provenance as well as non-answers, (ii) we can define explanations as either data in the input relations or relational operations in a query plan, and (iii) we can give graded degrees of responsibility to individual causes, thus allowing us to rank causes. In particular, our approach allows us to explain contributions to relational aggregate functions and to rank causes according to their respective responsibilities. We give complexity results and describe polynomial algorithms for evaluating causality in tractable cases. Throughout the paper, we illustrate the applicability of our framework with several examples.   Overall, we develop in this paper the theoretical foundations of causality theory in a database context.
---
PDF链接：
https://arxiv.org/pdf/0912.5340