Why so? or Why no? Functional Causality for Explaining Query Answers

TL;DR

This paper introduces a causality-based framework for explaining query answers and non-answers in databases, extending provenance and missing result explanations with a refined, property-rich definition of causes.

Contribution

It develops functional causes as a refined causality concept, enabling uniform explanations of query results, non-results, and contributions, including aggregate functions, with complexity analysis and algorithms.

Findings

Framework unifies explanations for query answers and non-answers.

Polynomial algorithms for causality evaluation in tractable cases.

Ability to rank causes by responsibility and explain aggregate contributions.

Abstract

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.