Selection-Bias-Corrected Visualization via Dynamic Reweighting
Selection-Bias-Corrected Visualization via Dynamic Reweighting
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DOI：
10.1109/tvcg.2020.3030455
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发表时间：
2021-02-01
影响因子：
5.2
通讯作者：
Gotz, David
中科院分区：
文献类型：
--
作者：
Borland, David;Zhang, Jonathan;Gotz, David
从复杂系统中收集和可视化分析大规模数据，如电子健康记录或点击流数据，在各行各业中越来越普遍。然而，这种类型的回顾性视觉分析容易产生各种选择偏差效应，特别是对于在任何给定时间仅可视化维度子集的高维数据。当分析师在临时分析期间动态应用过滤器或执行分组操作时，选择偏差的风险甚至更高。这些偏差效应威胁到视觉分析过程中发现的作为决策基础的见解的有效性和普遍性。过去的工作集中在偏见透明度，帮助用户了解选择偏见可能发生的时间。然而，通过偏差缓解来抵消选择偏差的影响通常由用户作为一个单独的过程来完成。动态重新加权（DR）是一种新的计算方法，以减轻选择偏差，帮助用户制作偏差校正的可视化。本文描述了DR工作流程，介绍了关键的DR可视化设计，并提出了支持DR过程的统计方法。还报告了来自医疗领域的用例以及领域专家用户访谈的结果。
The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threaten the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.