Collaborative Research: Generalized Propensity Score Methods

合作研究：广义倾向评分方法

• 批准号: 0550980
• 负责人: David van Dyk
• 金额: $ 20.51万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0550980&HistoricalAwards=false
• 关键词: Collaborative; Research; Generalized; Propensity; Score

It is well known that randomized treatment assignment can dramatically strengthen the force of causal inferences. Unfortunately, there is a wide array of scientific questions where ethical or practical concerns prohibit randomized treatments. It is in this context that nonparametric methods, such as matching and subclassification, are used to help adjust for pretreatment differences between the treatment and control groups. This project will extend the propensity score methods, which are widely used in applied research in order to conduct matching and subclassification with a large number of covariates, to a larger class of problems while maintaining their key advantages. Specifically, the study will (1) develop a generalized propensity score that is designed to handle more general treatment regimes, including categorical, ordinal, continuous, and multivariate treatments and (2) extend the use of propensity score methods to adjust for pretreatment measurements in randomized experiments in order to reduce the post-hoc bias that can be introduced by the choice of adjustment methods in typical data analyses.Observational studies play a key role in scientific investigation when results from ideal randomized experiments are not available. When practical or ethical concerns prevent randomized exposure to a supposed causal variable, such as smoking or an environmental hazard, scientists must rely on observational studies. Unfortunately, observational studies are difficult to analyze and can be riddled with biases since individuals who happen to be exposed to a supposed causal variable may be quite different from those who are not exposed. The significance of this research lies in an extension of the methods that have proved themselves highly useful in avoiding these biases. The effectiveness of the generalized propensity score methods will be illustrated through three concrete examples from medical and social science research: (1) a study of the effects of summer reading programs on autumn reading scores; (2) an investigation into the effectiveness of a proposed treatment for Fabry disease; and (3) the estimation of the causal effect of exposure to policy proposals on voting behavior. The new methods should have other applications throughout the physical, biological, and social sciences where causal inference is required with more complex causal variables than allowed for with current methods. The project also will extend methods to handle missing data and exploit some advantages of these methods for bias reduction in experimental settings.

众所周知，随机治疗分配可以极大地增强因果推论的力量。 不幸的是，存在大量的科学问题，其中伦理或实际问题禁止随机治疗。 正是在这种背景下，使用匹配和子分类等非参数方法来帮助调整治疗组和对照组之间的治疗前差异。 该项目将把广泛应用于应用研究中的倾向评分方法扩展到更大的问题类别，同时保持其主要优势，以便对大量协变量进行匹配和子分类。 具体来说，该研究将（1）开发一个广义倾向评分，旨在处理更一般的治疗方案，包括分类、有序、连续和多变量治疗；（2）扩展倾向评分方法的使用，以调整随机实验中的治疗前测量值，以减少典型数据分析中选择调整方法可能引入的事后偏差。观察性研究在科学中发挥着关键作用。 当无法获得理想的随机实验结果时进行调查。 当实际或道德问题阻止随机暴露于假定的因果变量（例如吸烟或环境危害）时，科学家必须依赖观察性研究。 不幸的是，观察性研究很难分析，并且可能充满偏见，因为碰巧接触到假定因果变量的个体可能与未接触过的个体有很大不同。 这项研究的意义在于扩展了已证明在避免这些偏见方面非常有用的方法。 广义倾向评分方法的有效性将通过医学和社会科学研究中的三个具体例子来说明：（1）夏季阅读计划对秋季阅读成绩影响的研究； (2) 对法布里病拟议治疗方法有效性的调查； (3) 政策提案暴露对投票行为的因果影响的估计。 新方法应该在整个物理、生物和社会科学领域有其他应用，这些领域需要比当前方法更复杂的因果变量进行因果推断。 该项目还将扩展处理缺失数据的方法，并利用这些方法的一些优点来减少实验设置中的偏差。