The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Case Method: Entity Relationship Modelling
Case Method: Entity Relationship Modelling
Building large knowledge bases by mass collaboration
Proceedings of the 2nd international conference on Knowledge capture
Distinguishing causal and acausal temporal relations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Beyond prediction: directions for probabilistic and relational learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
A rough set approach to mining connections from information systems
Proceedings of the 2010 ACM Symposium on Applied Computing
A rough set approach to multiple dataset analysis
Applied Soft Computing
Causal discovery in social media using quasi-experimental designs
Proceedings of the First Workshop on Social Media Analytics
Exploring social influence via posterior effect of word-of-mouth recommendations
Proceedings of the fifth ACM international conference on Web search and data mining
Transforming graph data for statistical relational learning
Journal of Artificial Intelligence Research
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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.