D. Jensen, A. Fast, B. Taylor, and M. Maier (2008). Automatic identification of quasi-experimental designs for discovering causal knowledge. Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Abstract

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 designs that were identified automatically. We demonstrate that QEDs can be identified in a traditional database schema and that such identification requires only a small number of extensions to that schema, knowledge about quasi-experimental design encoded in first-order logic, and a theorem-proving engine. We describe several key innovations necessary to enable this system, including methods for automatically constructing appropriate experimental units and for creating aggregate variables on those units. We show that applying the resulting designs can identify important causal dependencies in real domains, and we provide examples from academic publishing, movie making and marketing, and peerproduction systems. Finally, we discuss the integration of QEDs with other approaches to causal discovery, including joint modeling and directed experimentation.

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