摘要翻译：
消费者通过多种设备、浏览器和机器与公司进行交互；对于同一个消费者，这些交互通常是用不同的标识符记录的。未能正确匹配不同的身份导致对暴露和行为的支离破碎的看法。本文研究了同一性碎片偏差，即使用碎片数据所产生的估计偏差。利用一个形式化的框架，我们分解了由数据碎片引起的估计偏差的成因，并讨论了偏差的方向。与传统智慧相反，这种偏见不能在标准假设下签名或受限。相反，即使在实验环境中，也会出现向上偏差和符号反转。然后我们比较了几种纠正措施，并讨论了它们各自的优点和注意事项。
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英文标题：
《The Identity Fragmentation Bias》
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作者：
Tesary Lin and Sanjog Misra
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最新提交年份：
2021
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分类信息：

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：
  Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same consumer. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias, referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We then compare several corrective measures, and discuss their respective advantages and caveats.
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PDF链接：
https://arxiv.org/pdf/2008.12849