Identification of Causal Effects in the Context of Mass Collaboration

  • Olga SlivkoEmail author
  • Michael Kummer
  • Marianne Saam
Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 16)


Several instances of successful online mass collaboration have recently generated large amounts of data. These datasets are very appealing for empirical research on patterns and drivers of mass collaboration in a wide range of social science disciplines. However, their complexity, the presence of network effects, and multidirectional nature of the causal mechanisms at play often raise substantial challenges to empirical researchers. In this chapter, we discuss the econometric approach to mass collaboration, focusing on the methodological challenges of causal identification and the interpretation of how some factors affect others. Our chapter provides methodological tools for causal identification of effects in observational data from mass collaboration platforms. Specifically, we present two quasi-experimental methods, natural experiments and instrumental variables, in detail and show applications using examples from our own research.


Mass collaboration Collaboration Causal effects Natural experiments Econometric research 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Centre for European Research (ZEW)MannheimGermany

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