Collaborative Research: Generalized Propensity Score Methods

众所周知，随机化的治疗分配可以显著地加强因果推论的力量。不幸的是，有大量的科学问题，在伦理或实际考虑禁止随机治疗。正是在这种情况下，非参数方法，如匹配和子分类，被用来帮助调整治疗组和对照组之间的预处理差异。本项目将在保持其关键优势的同时，将应用研究中广泛使用的倾向得分方法扩展到更大的问题类别，以进行大量协变量的匹配和子分类。具体而言，本研究将(1)开发一种广义倾向评分，用于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预处理测量，以减少在典型数据分析中选择调整方法可能引入的事后偏差。在没有理想的随机实验结果时，观察性研究在科学调查中起着关键作用。当实际或伦理考虑阻止随机暴露于假定的因果变量（如吸烟或环境危害）时，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大不相同。这项研究的意义在于扩展了已经证明自己在避免这些偏差方面非常有用的方法。本文将通过医学和社会科学研究的三个具体实例来说明广义倾向评分方法的有效性：(1)夏季阅读计划对秋季阅读成绩的影响研究；（二）对拟治疗法布里病的疗效进行调查；(3)政策建议暴露对投票行为的因果效应估计。新方法应该在物理、生物和社会科学中有其他应用，在这些领域，因果推理需要比当前方法更复杂的因果变量。该项目还将扩展处理缺失数据的方法，并利用这些方法在实验环境中减少偏差的一些优势。

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.