Identification of Causal Effects in the Context of Mass Collaboration
Identification of Causal Effects in the Context of Mass Collaboration
复制标题
大规模协作背景下因果效应的识别
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Marianne Saam
中科院分区：
文献类型：
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作者：
Olga Slivko;Michael E. Kummer;Marianne Saam
成功的在线大规模合作的几个实例最近产生了大量数据。这些数据集非常吸引人们对广泛社会科学学科的大规模合作模式和驱动因素的实证研究。但是，它们的复杂性，网络效应的存在以及作用的因果机制的多向性质通常会给经验研究人员带来重大挑战。在本章中，我们讨论了大规模合作的计量经济学方法，重点关注因果鉴定的方法论挑战以及对某些因素如何影响其他因素的解释。我们的章节提供了从大众协作平台观察数据中效应的因果鉴定的方法论工具。具体而言，我们详细介绍了两种准实验方法，自然实验和仪器变量，并使用我们自己的研究中的示例显示应用程序。
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.