Instrumental Variable Methods for Observational Studies

工具变量（IV）方法是一种在存在不可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，一个核心问题是IV并不完全有效，因为它与未测量的混杂因素相关。 该项目将有助于改进使用IV方法的方法。 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。 一种设计IV研究的新方法，使研究对拟议的IV无效不太敏感（即，与未测量的混杂因素相关）。 该方法将涉及在IV水平高的一组受试者和IV水平低的一组受试者之间建立匹配比较，以使IV是两组中接受治疗的强预测因子。 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，更可靠。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。 出于实践和/或伦理原因，大多数此类研究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，由于治疗不是随机分配的，接受不同治疗的受试者可能不具有可比性，因此治疗后的不同结局可能不是治疗引起的效应。 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淆。 其基本思想是使用一个“工具”变量来提取治疗中与未测量混杂因素无关的变异，然后使用该变异来估计治疗对结果的因果影响。 该项目将提供更好地评估使用IV方法的结果对建议的IV与未测量的混杂因素（因此不是有效的IV）相关的担忧的敏感性的方法，以及当研究结果是二进制变量时使用IV的更好方法。 该项目还将开发和传播可免费获得的软件，用于实施新方法。 通过在不适合实验的复杂环境中提供严格的分析，改进的观察性研究方法有可能导致公共和私人机构的政策和做法得到改进。

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.