Collaborative Research: Shifting Paradigms: Using Subset Selection to Obtain Matched Samples

协作研究：转变范式：使用子集选择来获取匹配样本

• 批准号: 0849223
• 负责人: Wendy Cho
• 金额: $ 27.88万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0849223&HistoricalAwards=false
• 关键词: Collaborative; Research; Shifting; Paradigms; Using

Experimental studies are powerful because the experimental framework allows one to examine causal effects. Some research questions, however, are not amenable to an experimental framework. For example, if one is interested in whether long-term smoking causes lung cancer, it is not possible to randomly assign people to smoke for 30 years while randomly assigning others to not smoke for 30 years. Instead, this research must rely on observational data where one simply observes the rate of lung cancer among smokers and non-smokers who have made individual decisions about whether they will smoke. To get around the lack of randomization, researchers have attempted techniques that manipulate observational data to resemble the randomized experimental framework by matching individuals from the treatment group to individuals from the control group who are similar in measurable ways (age, income, education level, previous medical history, etc.) except that the treated individual smokes while the control individual does not. There is no consensus on how best to proceed in matching individuals, and the problem grows increasingly difficult as one tries to match on larger numbers of attributes. This project provides a novel formulation of the matching problem. The key insight is that matching individuals is neither necessary nor sufficient for simulating randomization. In an experiment, randomization ensures that the treatment group and the control group do not differ systematically on any attribute, but there does not need to be a ?twin? in the control group for each treated subject. The proposed procedure ensures systematically similar treatment and control groups by choosing treatment and control groups that maximize similarity in attributes. Moving away from the individual ?twin? approach allows for the exploration of a wider range of possible and better suited treatment and control groups. The PIs formulate procedures for addressing this problem that are superior to existing methods.Enhancing the ability to make causal inferences from observational data will stimulate research in a wide variety of fields and enhance our understanding of a broad array of phenomena. In political science, research on causal relationships include, but are not limited to, understanding the role of information on voters in advanced versus new democracies, the impact of different voting technologies for counting votes, whether proportional rather than majoritarian electoral systems are more effective for incorporating underrepresented groups, the extent to which degrees of campaign exposure affect the type of information individuals possess about politics, whether voter canvassing efforts are effective, and the effect of affirmative action on passing bar exams. The research questions are important and diverse, and the potential applications are limitless given a proper research design. In medicine or health, causal inference studies include applications to criminality rates related to gene patterns, the effect of generic substitution of presumptively chemically equivalent drugs, and the effect of maternal smoking on birth weight, to name but a few. Studies are certainly not limited to political science and health, and it would be simple to compile similar lists for a varied set of interesting and pressing queries in many other fields of study. The value of this research and its potential impact affects a diverse scholarly community.

实验研究是强大的，因为实验框架允许人们检查因果关系。然而，一些研究问题并不适用于实验框架。例如，如果一个人对长期吸烟是否会导致肺癌感兴趣，就不可能随机分配一个人吸烟30年，而随机分配另一个人30年不吸烟。相反，这项研究必须依赖于观察数据，在这些数据中，人们只需观察吸烟者和非吸烟者的肺癌发病率，这些吸烟者和非吸烟者已经就是否吸烟做出了个人决定。为了避免随机化的缺乏，研究人员尝试了一些技术，通过将来自治疗组的个人与在可测量方面(年龄、收入、教育水平、既往病史等)相似的控制组的个人进行匹配，来操纵观察数据以类似于随机实验框架。只是受试者吸烟，而对照组不吸烟。对于如何最好地进行个体匹配还没有达成共识，随着人们试图在更多的属性上进行匹配，这个问题变得越来越困难。这个项目提供了匹配问题的一种新的公式。关键的见解是，匹配的个体既不是模拟随机化的必要条件，也不是充分条件。在一项实验中，随机化确保了治疗组和对照组在任何属性上不存在系统性差异，但不需要有孪生兄弟。每名受试者均为对照组。建议的程序通过选择在属性上最大限度地相似的处理组和对照组来确保系统地相似的处理组和对照组。远离个体？双胞胎？方法允许探索更广泛的可能和更适合的治疗和对照小组。PI制定了比现有方法更好的解决这个问题的程序。增强从观测数据做出因果推断的能力将刺激广泛领域的研究，并加强我们对广泛现象的理解。在政治学中，对因果关系的研究包括但不限于：了解先进民主国家和新兴民主国家选民信息的作用；不同投票技术对计票的影响；比例选举制度而不是多数选举制度对于吸纳代表性不足的群体是否更有效；竞选活动的曝光程度在多大程度上影响个人拥有的关于政治的信息类型；选民拉票工作是否有效；以及平权行动对通过律师考试的影响。研究问题是重要的和多样的，只要有适当的研究设计，潜在的应用是无限的。在医学或健康领域，因果推断研究包括与基因模式相关的犯罪率的应用，推定的化学等效药的仿制替代的影响，以及母亲吸烟对新生儿体重的影响，仅举几例。研究当然不局限于政治学和卫生领域，为许多其他研究领域的各种有趣和紧迫的问题编制类似的清单将是很简单的。这项研究的价值及其潜在影响影响着不同的学术界。