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

ATD：协作研究：利用时空数据对冲突环境下的人类动态进行因果推断

• 批准号: 2124463
• 负责人: Kosuke Imai
• 金额: $ 10万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124463&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Causal; Inference

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.

在阿富汗和伊拉克这样的暴力环境中，空袭如何影响叛乱分子随后的暴力地点和性质？叛乱分子的暴力如何应对平民伤亡？大规模的援助计划如何影响这些环境中叛乱暴力的频率、类型和地点？这些问题是决策者每天面临的重要问题的例子。然而，尽管对人类暴力动力学的研究在过去十年中有了巨大的发展，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，学者们继续依赖的方法没有充分考虑政府、叛乱分子和平民之间快速互动的时空动态。现有的方法通常聚集细粒度的地理空间数据在更粗糙的时间和空间单位，扔掉了当前的微观层面的数据的优势，使学者无法利用未来的改进，高频，高分辨率的地理空间数据。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有效性进行错误的因果推断，使政策制定者看不到拟议政策可能产生的意外后果和负面外部性。它定义了在随机干预下感兴趣的因果量，这些因果量代表了反事实的治疗分配策略。估计策略采用基于估计的倾向分数表面的逆概率加权。除其他外，该项目将为这些复杂的时空设置开发中介分析，效果修改和敏感性分析。它还将调查其他重要问题，如最佳治疗分配，治疗溢出的空间范围，以及在持续治疗的存在下的因果关系推断。该项目小组将以脆弱环境中的空袭和经济援助为经验范例。目标是在领先的普通科学，统计和政治科学期刊上发表文章。参与者还将发布软件包，以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。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。