Collaborative Research: Propensity Scores and Randomization-Based Inference, or Modeling Assignment to Treatment Conditions as Manifestly or Latently Random

该项目包括方法学研究，以帮助分析比较研究；即，将“治疗”对象与对照进行比较以评估治疗效果的研究。它的出发点是与唐纳德·鲁宾和保罗·罗森鲍姆等人有关的一种有影响力的方法。在这种方法中，如果这样的研究可以被假定为潜伏的随机化--条件是协变量，治疗被分配好像是随机的--那么样本被重新组织成适当相似的区组，并且随后的分析进行，就好像至少在这些区组内，治疗的分配是明显随机的。近年来，以这种方式重组样本的技术，如配对和倾向评分，受到了方法论上的极大关注，但将转换后的样本视为明显随机的对待是否以及何时推论有效，仍不完全清楚。例如，在倾向-分数匹配方面，现有的方法学文献对倾向分数的估计不确定性、对其匹配的不精确度以及如何确定这些误差是否可能大到足以使调整无效的问题相对沉默。本项目的主要目的是利用与广泛使用的倾向分层形式相兼容的这些错误的新特征，对某一特定倾向匹配或分层的适宜性进行诊断。其次，该项目假设明显的随机化研究，以及那些被视为随机化的研究，将使用仅依赖于随机化特性的方法进行分析，而不是基于模型的方法。这些方法不同于社会科学中最常用的分析比较研究的方法，但它们可以建立在更知名的方法的基础上，并从更知名的方法中借鉴力量，这一项目也将阐明这一点。该项目的方法进步将在社会科学应用中得到展示，并将在高质量的开源软件中免费提供。该项目的一个鼓舞人心的前提是，好的统计方法不仅更好地满足使用这些方法的专家的需求，而且使非专家能够更容易和准确地评估这些专家提供的量化证据。因此，这项研究涉及一种统计调整，即匹配，它以其简单性和对非技术受众的吸引力而闻名。由于它和相关方法已经相当流行，该项目开发的方法和方法扩展将立即和直接适用于社会科学和行为科学中的任何数量的实证调查，其中许多具有公共卫生或公共政策影响。这些方法强调诊断学，以及基于诊断学试图证实的相同独立假设的统计结论，这可能有助于揭开它们所有助于的经验性调查的统计组成部分的神秘面纱，并使特定研究的证据优势和劣势变得透明。该项目还寻求让这些方法本身更广泛地可用。通过生成免费可用的软件，它将使所有如此倾向于在自己的分析中使用这些技术的人能够使用。通过将本科生和研究生纳入研究，有助于培养未来的定量社会科学方法论专家。因为它包括了一项向代表不足的少数族裔大学生特别推销这一机会的计划，它是对其他人在代表不足的群体中培养研究专长的努力的补充。

The project comprises methodological research to aid the analysis of comparative studies; that is, studies comparing "treated" subjects to controls in order to estimate effects of the treatment. Its starting point is an influential approach associated with Donald Rubin and Paul Rosenbaum, among others. In this approach, if such a study can be assumed latently randomized -- conditional on the covariate, treatment is assigned as if at random -- then the sample is reorganized into suitably similar blocks, and later analysis proceeds as if assignment to treatment had been manifestly at random, at least within these blocks. Techniques with which to reorganize samples in this way, such as matching and propensity scoring, have received much methodological attention in recent years, but whether and when it is inferentially valid to treat the transformed sample as if it had manifestly been randomized remains incompletely understood. In propensity-score matching, for example, extant methodological literature is relatively silent on estimation uncertainty in the propensity score, on imprecision in matching on it, and on how to determine whether these errors may be large enough to invalidate the adjustment. The primary aim of the present project is to use new characterizations of these errors, which are compatible with widely used forms of propensity stratification, to develop diagnostics for the suitability of a given propensity matching or stratification. Secondarily, the project assumes that manifestly randomized studies, along with those being treated as such, are to be analyzed with methods that rely only on properties of randomization, rather than model-based methods. These methods differ from those most commonly used in the social sciences to analyze comparative studies, but they can build on and borrow strength from better-known methods as this project will also make clear. The project's methodological advances will be demonstrated in social science applications, and will be made freely available in high-quality open source software. A motivating premise of the project is that good statistical methods not only better meet the needs of specialists who use them but also enable nonspecialists to more readily and accurately appraise the quantitative evidence those specialists produce. The research therefore concerns itself with a type of statistical adjustment, matching, that is distinguished for its simplicity and appeal to non-technical audiences. Because it and related methods are already quite popular, the methods and methodological extensions developed by the project will be immediately and directly applicable to any number of empirical investigations in the social and behavioral sciences, many of which have public health or public policy implications. These methods' emphasis on diagnostics, and on statistical conclusions based on the same independence assumptions that the diagnostics seek to corroborate, may help to demystify the statistical component of empirical investigations to which they contribute, and to make transparent the evidential strengths and weaknesses of particular studies. The project also seeks to make the methods themselves more broadly available. By generating freely available software, it will enable all who are so inclined to use the techniques in their own analyses. By including undergraduate and graduate students in the research, it contributes to the training of future quantitative social science methodologists. Because it includes a plan for special marketing of this opportunity to underrepresented minority college students, it complements efforts by others to cultivate research expertise among underrepresented groups.