论文标题
身份碎片偏见
The Identity Fragmentation Bias
论文作者
论文摘要
消费者与跨多个设备，浏览器和机器的公司互动；这些相互作用通常与同一消费者的不同标识符一起记录。无法正确匹配不同身份的情况会导致暴露和行为的零散视图。本文研究了身份片段化偏差，指的是使用碎片数据引起的估计偏差。使用正式框架，我们分解了由数据碎片引起的估计偏差的因素，并讨论了偏差的方向。与传统的观点相反，这种偏见不能在标准假设下签署或界定。取而代之的是，即使在实验设置中，也可能发生向上的偏见和符号逆转。然后，我们比较了几种纠正措施，并讨论它们各自的优势和警告。
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.
