Unpacking Compound Treatments in Email Audit Experiments

解析电子邮件审计实验中的复合处理

• 批准号: 2217522
• 负责人: Nicole Pashley
• 金额: $ 40.39万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217522&HistoricalAwards=false
• 关键词: Unpacking; Compound; Treatments; Email; Audit

This research project will improve the design and analysis of audit experiments. Audit experiments are tools used to test for discriminatory behavior in situations where surveys and interviews can induce social desirability bias and are known to make strong causal claims. However, a common issue in audit experiments is the potential confounding of interventions with other attributes. For example, researchers often use names to signal race, but names may also signal other attributes, such as socio-economic status. Using other signals like income or occupation as additional interventions may help disentangle the effect of race from socio-economic status. This project will develop improved design and methodology for disentangling the effects of potential confounders from interventions of interest by incorporating such confounders as additional interventions. The methods to be developed will be applied to the study of the effects of race and related socio-economic factors on access to legal services. Understanding discrimination in the market for legal services is important because access to lawyers is a crucial first step for individuals to participate in the justice system. The project also will support training and engagement with audit studies through the involvement of graduate students.This research project will develop methods to address the complex problems in design and analysis that result from audit experiments having a factorial structure. These methods will include designing optimal factorial experiments that maximize information under resource constraints, development of appropriate statistical models for assessing causal effects of interventions, and data analysis procedures consistent with the design. The modern theory of causal inference and randomized experiments will serve as the foundation for the new methods. Analysis methods will be developed to assess causal effects of interventions on a selected group of subjects (finite population) as well as on a larger superpopulation from which such subjects can be assumed to be randomly selected. Methods for model-free (randomization-based) and model-based assessment of causal effects will be developed. The investigators also will explore methodology to extend design and analysis of audit experiments with binary or two-level interventions to the case of multilevel interventions. The new methods will be applied to an audit experiment to assess racial discrimination in response to requests for legal services, using names on emails to signal race. The results of this research will provide critical evidence for the study of racial discrimination in law and access to the justice system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目将改进审计实验的设计和分析。审计实验是用于测试调查和访谈可能引起社会期望偏差并且已知会提出强烈因果主张的情况下的歧视行为的工具。然而，审计实验中的一个常见问题是干预措施与其他属性的潜在混淆。例如，研究人员经常使用名字来表示种族，但名字也可能表示其他属性，例如社会经济地位。使用收入或职业等其他信号作为额外的干预措施可能有助于区分种族与社会经济地位的影响。该项目将开发改进的设计和方法，通过将此类混杂因素纳入额外干预措施，将潜在混杂因素的影响与感兴趣的干预措施分开。待开发的方法将应用于研究种族和相关社会经济因素对获得法律服务的影响。了解法律服务市场中的歧视很重要，因为获得律师的帮助是个人参与司法系统的关键的第一步。该项目还将通过研究生的参与来支持审计研究的培训和参与。该研究项目将开发方法来解决具有因子结构的审计实验所产生的设计和分析中的复杂问题。这些方法将包括设计在资源限制下最大化信息的最佳因子实验，开发适当的统计模型来评估干预措施的因果效应，以及与设计一致的数据分析程序。现代因果推理和随机实验理论将成为新方法的基础。将开发分析方法来评估干预措施对选定的一组受试者（有限群体）以及对更大的超级群体的因果影响，可以假设这些受试者是从其中随机选择的。将开发无模型（基于随机化）和基于模型的因果效应评估方法。研究人员还将探索方法，将二元或两级干预的审计实验的设计和分析扩展到多级干预的情况。新方法将应用于一项审计实验，以评估针对法律服务请求的种族歧视，使用电子邮件中的姓名来表示种族。这项研究的结果将为研究法律中的种族歧视和诉诸司法系统提供关键证据。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。