Collaborative Research: Shifting Paradigms: Causal Inference via Subset Selection

实验研究是强大的，因为实验框架允许人们检查因果效应。然而，有些研究问题并不适合于实验框架。例如，如果一个人对长期吸烟是否会导致肺癌感兴趣，那么就不可能随机分配一个人吸烟30年，而随机分配另一个人不吸烟30年。相反，这项研究必须依赖于观察数据，其中一个简单地观察吸烟者和非吸烟者之间的肺癌发病率，这些人已经做出了是否吸烟的个人决定。为了解决缺乏随机化的问题，研究人员尝试了一些技术，通过将治疗组中的个体与对照组中在可测量方面（年龄，收入，教育水平，既往病史等）相似的个体进行匹配，来操纵观察数据，以类似于随机化的实验框架。除了治疗个体吸烟而对照个体不吸烟。在如何最好地进行个体匹配方面没有达成共识，并且当人们试图匹配大量属性时，问题变得越来越困难。该项目提供了一种新的配方的匹配问题。关键的见解是，匹配的个人既不是必要的，也不足以模拟随机化。在实验中，随机化确保治疗组和对照组在任何属性上都没有系统性差异，但不需要有一个？双胞胎？在对照组中，每例治疗受试者的平均体重均为100。所提出的程序通过选择属性相似性最大化的治疗组和对照组，确保系统相似的治疗组和对照组。从个体出发，？双胞胎？这种方法允许探索更广泛的可能且更适合的治疗和对照组。PI制定了解决这一问题的程序，这是上级现有的methods.Enhancing能力，使因果推论从观测数据将刺激在各种各样的领域的研究，并提高我们对广泛的现象的理解。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与新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是否更有效地纳入代表性不足的群体，竞选活动的曝光程度在多大程度上影响个人所掌握的政治信息类型，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。研究的问题是重要的和多样的，并给予适当的研究设计的潜在应用是无限的。在医学或健康领域，因果推理研究包括与基因模式相关的犯罪率、推定化学等效药物的非专利替代品的影响以及母亲吸烟对出生体重的影响等应用。研究当然不仅限于政治学和卫生，而且在许多其他研究领域为各种有趣和紧迫的问题编制类似的清单也很简单。这项研究的价值及其潜在影响影响着一个多元化的学术界。

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.