Quantifying Causal Effects on Query Answering in Databases

Abstract

The notion of actual causation, as formalized by Halpern and Pearl, has been recently applied to relational databases, to characterize and compute actual causes for possibly unexpected answers to monotone queries. Causes take the form of database tuples, and can be ranked according to their causal responsibility, a numerical measure of their relevance as a cause to the query answer. In this work we revisit this notion, introducing and making a case for an alternative measure of causal contribution, that of causal effect. The measure generalizes actual causes, and can be applied beyond monotone queries. We show that causal effect provides intuitive and intended results.

arXiv commentTo appear in Proc. TAPP'16

References in corpus (4)

• Causality and the Semantics of Provenance
• From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
• Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
• Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints

Cited by in corpus (7)

• The Shapley Value of Tuples in Query Answering
• The Shapley Value in Database Management
• Uniform Reliability of Self-Join-Free Conjunctive Queries
• Causal Relational Learning
• The Causality/Repair Connection in Databases: Causality-Programs
• Reasoning about Counterfactuals and Explanations: Problems, Results and Directions
• Data Management for Causal Algorithmic Fairness