Instrumental Variable Methods for Observational Studies

观察研究的工具变量方法

• 批准号: 0961971
• 负责人: Dylan Small
• 金额: $ 14.47万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0961971&HistoricalAwards=false
• 关键词: Instrumental; Variable; Methods; Observational; Studies

The instrumental variable (IV) method is an approach to estimating a causal relationship in the presence of unmeasured confounders. A central concern in most studies using the IV method is that the IV is not perfectly valid in the sense that it is correlated with unmeasured confounders. This project will contribute to improved methodology for using the IV method. The project will develop a new, more interpretable sensitivity analysis for IV studies that is calibrated to observed covariates. A new way of designing IV studies to make the study less sensitive to the proposed IV being invalid (i.e., correlated with unmeasured confounders) also will be developed. The approach will involve setting up a matched comparison between a group of subjects with a high level of the IV and a group of subjects with a low level of the IV in such a way that the IV is a strong predictor of the treatment that is received in the two groups. Finally, a new IV method for studies with binary outcomes will be developed that is easier to implement and more robust than existing methods.A main goal of many empirical studies in the social sciences is to provide evidence about the effects caused by policies or treatments. For practical and/or ethical reasons, most such studies are observational rather than randomized studies. A central difficulty for observational studies is that because treatments were not randomly assigned, the subjects receiving different treatments may not be comparable so differing outcomes after treatment may not be effects caused by the treatment. The instrumental variable (IV) method is an approach for estimating a causal relationship that can overcome unmeasured confounding. The basic idea is to use an "instrumental" variable to extract variation in the treatment that is unrelated to the unmeasured confounders, and then use this variation to estimate the causal effect of the treatment on the outcome. This project will provide ways to better assess sensitivity of results from using the IV methods to concerns that the proposed IV is related to unmeasured confounders (and thus not a valid IV), and better ways to make use of an IV when the outcome of the study is a binary variable. The project also will develop and disseminate freely available software for implementing the new methods. By offering rigorous analysis in complex setting otherwise not suited for experimentation, improved methodology for observational studies has the potential to lead to improved policies and practices of both public and private institutions.

工具变量（IV）方法是一种在存在未测量混杂因素的情况下估计因果关系的方法。在大多数使用IV方法的研究中，一个中心问题是IV不是完全有效的，因为它与未测量的混杂因素相关。本项目将有助于改进IV法的使用方法。该项目将为IV研究开发一种新的，更可解释的敏感性分析，该分析根据观察到的协变量进行校准。还将开发一种设计静脉注射研究的新方法，使研究对所提出的静脉注射无效（即与未测量的混杂因素相关）不那么敏感。该方法将涉及在一组高水平静脉注射的受试者和一组低水平静脉注射的受试者之间建立一个匹配的比较，以使静脉注射成为两组接受治疗的有力预测指标。最后，将开发一种新的用于二元结果研究的IV方法，该方法比现有方法更容易实施且更稳健。社会科学中许多实证研究的主要目标是提供有关政策或治疗所造成的影响的证据。出于实际和/或伦理原因，大多数此类研究都是观察性研究，而不是随机研究。观察性研究的一个主要困难是，由于治疗方法不是随机分配的，接受不同治疗的受试者可能没有可比性，因此治疗后的不同结果可能不是由治疗引起的。工具变量（IV）方法是一种估计因果关系的方法，可以克服不可测量的混杂。其基本思想是使用“工具”变量来提取治疗中与未测量混杂因素无关的变化，然后使用该变化来估计治疗对结果的因果影响。本项目将提供更好地评估结果敏感性的方法，从使用IV方法到考虑拟议的IV与未测量的混杂因素有关（因此不是有效的IV），以及当研究结果是二元变量时更好地使用IV的方法。该项目还将开发和传播用于实施新方法的免费软件。通过在复杂的环境中提供严格的分析，否则不适合实验，观察研究的改进方法有可能导致公共和私人机构的政策和实践的改进。