Automatic identification of quasi-experimental designs for discovering causal knowledge

Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large databases. Quasi-experimental designs (QEDs) exploit fortuitous circumstances in non-experimental data to identify situations (sometimes called "natural experiments") that provide the equivalent of experimental control and randomization. QEDs allow researchers in domains as diverse as sociology, medicine, and marketing to draw reliable inferences about causal dependencies from non-experimental data. Unfortunately, identifying and exploiting QEDs has remained a painstaking manual activity, requiring researchers to scour available databases and apply substantial knowledge of statistics. However, recent advances in the expressiveness of databases, and increases in their size and complexity, provide the necessary conditions to automatically identify QEDs. In this paper, we describe the first system to discover knowledge by applying quasi-experimental de...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: David D. Jensen, Andrew S. Fast, Brian J. Taylor, Marc E. Maier
• Keywords: Data Mining, Important Causal Dependencies, KDD 2008, QEDs Allow Researchers, Traditional Database Schema