A Bayesian Approach to Selection Bias Applied to Racial Profiling

应用于种族分析的贝叶斯选择偏差方法

• 批准号: 1024389
• 负责人: Katherine Barnes
• 金额: $ 17万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1024389&HistoricalAwards=false
• 关键词: Bayesian; Approach; Selection; Bias; Applied

This project proposes to investigate new statistical methods to control for selection bias when faced with significant data limitations. In general, using non-random samples of data can lead to quite biased estimates of model parameters. This, in turn, can lead to incorrect conclusions based on systematically biased estimates. This project controls for selection bias in regression analysis, with an application to the racial profiling context. Specifically, the project proposes a statistical model that will estimate a selection model without the need for individual-level data for the non-selected individuals. The first half of the project quantifies the potential loss in precision from using aggregate-level data rather than individual-level data when controlling for selection bias. The second half of the project applies the new model to the actual selection problem of racial profiling, modeling the selection for search of cars driving down the highway (rather than just the subset of stopped cars). This model will quantify the costs and benefits of racial profiling, including the rate of searching innocent motorists and the drugs seized from such stops. While these empirical results will not definitively answer whether racial profiling as a policy meets the high demands of the constitution, it will provide a beginning to answer this question with unbiased empirical results. Although the project focuses on selection bias in the racial profiling context, the method used has wide applicably to many empirical questions, and provides a significant step in controlling for selection bias when individual-level data is unavailable.

该项目建议研究新的统计方法，以在面临重大数据限制时控制选择偏差。一般来说，使用非随机数据样本可能会导致模型参数的估计存在很大偏差。反过来，这可能会导致基于系统性偏差估计得出错误的结论。该项目控制回归分析中的选择偏差，并应用于种族定性背景。具体来说，该项目提出了一种统计模型，可以估计选择模型，而不需要未选择个体的个体级别数据。该项目的前半部分量化了在控制选择偏差时使用聚合级数据而不是个体级数据所带来的潜在精度损失。该项目的后半部分将新模型应用于种族定性的实际选择问题，对在高速公路上行驶的汽车（而不仅仅是停下来的汽车的子集）的搜索选择进行建模。该模型将量化种族定性的成本和收益，包括搜查无辜驾车者的比率以及从此类站点查获的毒品。虽然这些实证结果不能明确回答种族定性作为一项政策是否满足宪法的高要求，但它将为以公正的实证结果回答这个问题提供一个开始。尽管该项目侧重于种族定性背景下的选择偏差，但所使用的方法广泛适用于许多实证问题，并且在无法获得个人数据时为控制选择偏差提供了重要的一步。