document: 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 data-bases . 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 peer-production systems . Finally , we discuss the integration of QEDs with other approaches to causal discovery , including joint modeling and directed experimentation .
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