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摘要翻译：
随机实验或a/B测试通过创建两个平行宇宙来估计一个特征对用户行为的因果影响，其中成员同时被分配到治疗和控制。然而，在社交网络设置中，成员进行交互，因此一个功能的影响并不总是包含在治疗组中。研究人员已经开发了许多实验设计来估计社会环境中的网络效应。或者，自然发生的外源变异，或者“自然实验”，允许研究人员在没有实验操作的情况下，从观察数据中恢复对同伴效应的因果估计。自然实验权衡了工程成本和一些与网络随机化相关的伦理问题，以及寻找具有自然外生变异的情况的搜索成本。为了降低与发现自然反事实相关的搜索成本，我们确定了一个用于扩展大规模在线系统的常见工程需求：通知排队，在这些系统中可能存在自然的外生变化。我们在LinkedIn平台上确定了两个基于通知队列顺序的自然实验，以估计接收到的消息对接收者参与的因果影响。我们表明，从另一个成员接收消息会显著增加一个成员的参与，但一些流行的观察规范，如固定效应估计器，高估这种效应高达2.7倍。然后，我们将估计的网络效应系数应用于大量过去的实验，以量化它在多大程度上改变了我们对实验结果的解释。这项研究指出了使用消息队列来发现自然发生的反事实的好处，以便在没有实验者干预的情况下估计因果效应。
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英文标题：
《Estimating Network Effects Using Naturally Occurring Peer Notification
Queue Counterfactuals》
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作者：
Craig Tutterow and Guillaume Saint-Jacques
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最新提交年份：
2019
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Social and Information Networks 社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
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一级分类：Computer Science 计算机科学
二级分类：Computers and Society 计算机与社会
分类描述：Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
涵盖计算机对社会的影响、计算机伦理、信息技术和公共政策、计算机的法律方面、计算机和教育。大致包括ACM学科类K.0、K.2、K.3、K.4、K.5和K.7中的材料。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
Randomized experiments, or A/B tests are used to estimate the causal impact of a feature on the behavior of users by creating two parallel universes in which members are simultaneously assigned to treatment and control. However, in social network settings, members interact, such that the impact of a feature is not always contained within the treatment group. Researchers have developed a number of experimental designs to estimate network effects in social settings. Alternatively, naturally occurring exogenous variation, or 'natural experiments,' allow researchers to recover causal estimates of peer effects from observational data in the absence of experimental manipulation. Natural experiments trade off the engineering costs and some of the ethical concerns associated with network randomization with the search costs of finding situations with natural exogenous variation. To mitigate the search costs associated with discovering natural counterfactuals, we identify a common engineering requirement used to scale massive online systems, in which natural exogenous variation is likely to exist: notification queueing. We identify two natural experiments on the LinkedIn platform based on the order of notification queues to estimate the causal impact of a received message on the engagement of a recipient. We show that receiving a message from another member significantly increases a member's engagement, but that some popular observational specifications, such as fixed-effects estimators, overestimate this effect by as much as 2.7x. We then apply the estimated network effect coefficients to a large body of past experiments to quantify the extent to which it changes our interpretation of experimental results. The study points to the benefits of using messaging queues to discover naturally occurring counterfactuals for the estimation of causal effects without experimenter intervention.
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PDF链接：
https://arxiv.org/pdf/1902.07133
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