The Causal-Effect Score in Data Management

TL;DR

This paper introduces and explores the Causal-Effect Score as a new metric for measuring the causal influence of data tuples in classical and probabilistic databases, aiming to improve query attribution.

Contribution

It generalizes the Causal-Effect concept and applies it to data management, providing a novel approach for tuple attribution in database query answering.

Findings

Proposes the Causal-Effect Score for data attribution

Extends CE to probabilistic databases

Demonstrates potential for improved query explanation

Abstract

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.