Statistical Methods for Causal Inference in Geographic Regression Discontinuity Designs

该研究项目将开发方法，以提高对地理参考观测研究的估计的理解。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，从而减少过度自信或可能误导公众的结果的机会。当一个人面对地理参考数据时，例如那些由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内的单位与洪水区外的单位来估计洪水对焦虑的影响。然而，潜在的、未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。解决这些问题的常见方法往往大大低估了不确定性，存在严重的偏差，或者依赖于非常强的建模假设，从而可能导致错误的结论和发现。本项目将通过开发在因果推理的背景下充分表征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，他们将主持一个以这个主题为重点的研讨会。该研究项目将因果关系和空间统计领域联系起来，为推断空间参考数据中的因果关系提供了一个统一的框架。研究人员将扩展回归不连续设计框架，将决定治疗的某个切点上方和下方的单元进行比较，以推断因果关系。例如，人们可能会比较洪水中高水位标志两侧的单位，假设由于地理位置接近，这些单位除了经历过洪水外，是相似的。然而，与经典的回归不连续不同，这里的边界是一条线而不是一个点，这大大增加了分析的复杂性。该项目将创建和评估灵活的工具来处理这些复杂性，并演示如何在现实环境中使用这些工具。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，将允许在处理边界上有效地随机分配单元。它还允许用相对较少和较弱的建模假设进行因果推理，这在观测数据环境中是至关重要的。

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.