Collaborative Research: Generalized Propensity Score Methods

众所周知，随机治疗分配可以极大地增强因果推断的力量。不幸的是，在许多科学问题中，伦理或实际问题阻碍了随机治疗。正是在这种背景下，非参数方法，如匹配和细分，被用来帮助调整治疗组和对照组之间的治疗差异。该项目将把应用研究中广泛使用的倾向评分方法扩展到更大类别的问题，同时保持其关键优势。具体地说，该研究将(1)开发一个广义倾向评分，旨在处理更一般的处理方案，包括分类、顺序、连续和多变量处理，以及(2)扩展倾向评分方法的使用，以调整随机实验中的前处理测量，以减少在典型数据分析中因调整方法的选择而引入的特殊后偏差。当理想随机实验的结果不可用时，观察性研究在科学研究中发挥关键作用。当实际或伦理方面的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，因为碰巧接触到假设的因果变量的人可能与没有接触的人有很大不同。这项研究的意义在于扩展了已被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来自医学和社会科学研究的三个具体例子来说明：(1)夏季阅读计划对秋季阅读分数的影响的研究；(2)对拟议的法布里病治疗方法的有效性的调查；以及(3)对暴露于政策建议对投票行为的因果影响的估计。新的方法应该在物理、生物和社会科学中有其他的应用，在这些科学中，需要用比当前方法所允许的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，并在实验环境中利用这些方法的一些优势来减少偏差。

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.