Design of Randomized Experiments in Networks
Design of Randomized Experiments in Networks
复制标题
网络随机实验的设计
DOI：
10.2139/ssrn.2477076
复制
发表时间：
2014
影响因子：
20.6
通讯作者：
Lev Muchnik
中科院分区：
文献类型：
--
作者：
Dylan Walker;Lev Muchnik
在过去十年中，无处不在的网络和数字化环境的出现创造了丰富的人类行为详细数据来源。然而，大数据的前景最近受到了抨击，因为它无法将相关性与因果关系分离开来，无法获得可操作的见解并产生有效的政策。幸运的是，我们日常互动的在线平台允许进行大规模的实验，从而引领了一场迈向大型实验的新运动。随机对照试验是科学方法的核心，如果设计得当，可以提供明确的、可靠的、可重复的因果推论。然而，认识到我们的世界是高度联系的，个人和人口层面的行为和经济结果取决于这种联系，这对实验设计的原则提出了挑战。因此，网络实验的适当设计和分析是至关重要的。在这项工作中，我们对设计和分析网络实验的新兴策略进行了分类和回顾，并讨论了它们的优缺点。
Over the last decade, the emergence of pervasive online and digitally enabled environments has created a rich source of detailed data on human behavior. Yet, the promise of big data has recently come under fire for its inability to separate correlation from causation-to derive actionable insights and yield effective policies. Fortunately, the same online platforms on which we interact on a day-to-day basis permit experimentation at large scales, ushering in a new movement toward big experiments. Randomized controlled trials are the heart of the scientific method and when designed correctly provide clean causal inferences that are robust and reproducible. However, the realization that our world is highly connected and that behavioral and economic outcomes at the individual and population level depend upon this connectivity challenges the very principles of experimental design. The proper design and analysis of experiments in networks is, therefore, critically important. In this work, we categorize and review the emerging strategies to design and analyze experiments in networks and discuss their strengths and weaknesses.