Collaborative Research: Generalized Propensity Score Methods

众所周知，随机治疗分配可以显著加强因果推理的力量。 不幸的是，有一系列广泛的科学问题，伦理或实际问题禁止随机治疗。 正是在这种情况下，非参数方法，如匹配和亚分类，用于帮助调整治疗组和对照组之间的治疗前差异。 本项目将在保持其主要优势的同时，将在应用研究中广泛使用的倾向评分方法扩展到更大类的问题，以进行大量协变量的匹配和子分类。 具体而言，该研究将（1）开发一种广义倾向评分，旨在处理更一般的治疗方案，包括分类、顺序、连续、和多变量治疗和（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，以减少治疗后在典型的数据分析中，由于调整方法的选择而引入的特殊偏差。当理想的随机实验结果不可用时，观察性研究在科学研究中起着关键作用。 当实际或伦理问题阻止随机暴露于假定的因果变量时，如吸烟或环境危害，科学家必须依靠观察性研究。 不幸的是，观察性研究很难分析，而且可能充满偏见，因为碰巧接触到假定的因果变量的个体可能与没有接触到的个体完全不同。 这项研究的意义在于扩展了那些已经证明在避免这些偏见方面非常有用的方法。 广义倾向评分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分数影响的研究;（2）对法布里病治疗方案有效性的调查;（3）对政策建议对投票行为的因果影响的估计。 新的方法应该有其他的应用在整个物理，生物和社会科学的因果推理需要更复杂的因果变量比允许与当前的方法。 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势来减少实验环境中的偏倚。

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.