Statistical Methods for Causal Inference in Geographic Regression Discontinuity Designs

地理回归不连续性设计中因果推断的统计方法

• 批准号: 1461435
• 负责人: Luke Miratrix
• 金额: $ 33万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-15 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1461435&HistoricalAwards=false
• 关键词: Statistical; Methods; Causal; Inference; Geographic

This research project will develop methods to improve understanding of estimates from geographically referenced observational studies. These methods will enable researchers to accurately assess uncertainty for this class of problems, thereby reducing the chance of over-confident or potentially misleading results being presented to the public. When one is faced with geographically referenced data, such as those collected by population registries or satellite imagery, attempts to infer causal relationships between treatments and outcomes often are thwarted by the complex underlying spatial structure. For example, one might wish to estimate the influence of flooding on anxiety by comparing units in the flood zone to units outside the flood zone. However, underlying, unmeasured, and geographically varying characteristics, such as socio-economic status, may confound this relationship. Common approaches to these problems often substantially underestimate uncertainty, have serious issues of bias, or rely on very strong modeling assumptions, potentially leading to erroneous conclusions and findings. This project will address these issues by developing methods that adequately characterize and model spatial variation in the context of causal inference. The researchers also will develop and release software for analysis, and they will host a workshop focusing on this topic. This research project bridges the fields of causality and spatial statistics and offers a unified framework for inferring causal relationships in spatially referenced data. The researchers will extend the regression discontinuity design framework, where units just above and below some cut-point that determines treatment are compared to infer a causal relationship. For example, one might compare those on either side of a high-water mark in a flood, with the assumption that due to their geographic proximity, such units, other than having experienced flooding, are similar. However, unlike classic regression discontinuity, here the boundary is a line rather than a point, which substantially complicates analysis. The project will create and evaluate flexible tools to handle these complications and demonstrate how to use these tools in real-world contexts. The project's most significant theoretical contribution will be to fit the slope of the response surface with respect to the boundary rather than the response surface itself, which allows for appropriate extrapolation. This enhancement, coupled with the flexible nature of fitting surfaces using spatial tools, will allow for the preservation of effective random assignment of units across a treatment boundary. It also will allow for causal inference with relatively few and weak modeling assumptions, something that is critical in an observational data context.

该研究项目将开发方法，以提高对地理参考观察研究估计值的理解。 这些方法将使研究人员能够准确地评估这类问题的不确定性，从而减少向公众展示过度自信或潜在误导性结果的机会。 当人们面对地理参考数据时，例如人口登记或卫星图像收集的数据，试图推断治疗和结果之间的因果关系往往会受到复杂的潜在空间结构的阻碍。 例如，人们可能希望通过比较洪水区和洪水区以外的单位来估计洪水对焦虑的影响。 然而，潜在的、不可测量的和地理上不同的特征，如社会经济地位，可能会混淆这种关系。 解决这些问题的常用方法往往大大低估了不确定性，存在严重的偏见问题，或者依赖于非常强的建模假设，可能导致错误的结论和发现。 该项目将通过开发在因果推理的背景下充分描述和模拟空间变化的方法来解决这些问题。 研究人员还将开发和发布用于分析的软件，并将举办一个专注于这一主题的研讨会。这个研究项目连接了因果关系和空间统计学领域，并提供了一个统一的框架来推断空间参考数据中的因果关系。 研究人员将扩展回归不连续性设计框架，其中将确定治疗的临界点以上和以下的单位进行比较，以推断因果关系。 例如，人们可能会比较洪水中高水位线两侧的那些单元，假设由于它们的地理位置接近，这些单元除了经历过洪水之外是相似的。 然而，与经典的回归不连续性不同，这里的边界是一条线而不是一个点，这大大复杂化了分析。 该项目将创建和评估灵活的工具来处理这些并发症，并演示如何在现实世界中使用这些工具。 该项目最重要的理论贡献将是拟合响应面相对于边界的斜率，而不是响应面本身，这允许适当的外推。 这种增强，再加上使用空间工具拟合表面的灵活性，将允许在治疗边界上保留有效的随机分配单元。 它还允许用相对较少和较弱的建模假设进行因果推理，这在观测数据背景下至关重要。