ATD: Collaborative Research: Causal Inference with Spatio-Temporal Data on Human Dynamics in Conflict Settings

在阿富汗和伊拉克等暴力环境中，空袭对叛乱分子随后发生暴力的地点和性质有何影响？叛乱暴力如何应对平民伤亡？在这些情况下，大规模援助计划如何影响叛乱暴力的频率、类型和地点？这些问题是政策制定者每天面临的重要问题的例子。然而，尽管在过去十年中，对人类暴力动态的研究有了很大的增长，但我们仍然缺乏在这些具有挑战性的环境中进行因果推理的方法。事实上，学者们继续依赖于不能完全解释政府、叛乱分子和平民之间快速互动的空间和时间动态的方法。现有的方法通常将细粒度的地理空间数据聚集在更粗的时间和空间单元上，放弃了当前微观层面数据的优势，使学者们无法利用未来对高频、高分辨率地理空间数据的改进。综上所述，现有的框架可能会对这些环境中暴力和非暴力干预的效果做出错误的因果推断，使政策制定者对拟议政策可能产生的意外后果和负面外部性视而不见。该项目将开发一个全面的时空因果推理框架，避免数据聚合，并且不对空间溢出和时间遗留效应强加任何结构性假设。它定义了随机干预下的因果量，代表了反事实的治疗分配策略。该估计策略使用基于估计的倾向记分面的逆概率加权。除其他外，该项目将为这些复杂的时空环境开发调解分析、效果修改和敏感性分析。它还将调查其他重要问题，如最优治疗分配、治疗溢出的空间范围，以及在存在持续治疗的情况下的因果推断。该项目团队将在脆弱的环境中使用空袭和经济援助作为经验例子。目标是在领先的普通科学、统计学和政治学期刊上发表文章。投资促进机构还将发布软件包，使时空框架自动化，并将其扩展到其他问题和环境。该项目将提供研究生水平的研究培训机会。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

How do airstrikes affect the subsequent location and nature of insurgent violence in violent settings like Afghanistan and Iraq? How does insurgent violence change in response to civilian casualties? And how do large-scale aid programs affect the frequency, type, and location of insurgent violence in these settings? These questions are examples of important issues that face policy-makers on a daily basis. Yet while the study of human dynamics of violence has grown enormously over the past decade, we still lack methods for conducting causal inference in these challenging settings. Indeed, scholars have continued to rely on approaches that do not fully account for the spatial and temporal dynamics that characterize fast-moving interactions between governments, insurgents, and civilians. Existing methods typically aggregate fine-grained geo-spatial data at much coarser temporal and spatial units, throwing away the advantages of current micro-level data and leaving scholars unable to capitalize on future improvements to high-frequency, high-resolution geo-spatial data. Taken together, existing frameworks risk mistaken causal inferences about the efficacy of both violent and non-violent interventions in these settings, leaving policy-makers blind to possible unintended consequences and negative externalities of proposed policies.This project will develop a comprehensive spatio-temporal causal inference framework that avoids data aggregation and imposes no structural assumptions on spatial spillover and temporal carryover effects. It defines causal quantities of interest under stochastic interventions, which represent counterfactual treatment assignment strategies. The estimation strategy employs inverse probability weighting based on an estimated propensity score surface. Among others, this project will develop mediation analysis, effect modification, and sensitivity analysis for these complex spatio-temporal settings. It will also investigate other important problems such as optimal treatment allocations, the spatial range of treatment spillover, and causal inference in the presence of persistent treatments. This project team will use airstrikes and economic assistance in fragile settings as empirical examples. The goal is to publish articles in leading general science, statistical, and political science journals. The PIs will also publish software packages to automate and to extend the spatio-temporal framework to other issues and settings. The project will provide research training opportunities at a graduate level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.