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: 2124323
• 负责人: Jason Lyall
• 金额: $ 10万
• 依托单位: Dartmouth College
• 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=2124323&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，空袭如何影响叛乱暴力的后续地点和性质？面对平民伤亡，叛乱分子的暴力行为会如何改变？大规模援助项目如何影响这些地区叛乱暴力的频率、类型和地点？这些问题都是决策者每天面临的重要问题的例子。然而，尽管对人类暴力动态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，学者们一直依赖的方法并不能完全解释政府、叛乱分子和平民之间快速互动的时空动态特征。现有的方法通常以更粗糙的时间和空间单位聚合细粒度的地理空间数据，丢掉了当前微观层面数据的优势，使学者无法利用高频、高分辨率地理空间数据的未来改进。总的来说，现有框架可能会对这些情况下暴力和非暴力干预措施的有效性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，以避免数据聚合，并且不对空间溢出和时间结转效应施加结构性假设。它定义了随机干预下的兴趣因果量，表示反事实处理分配策略。估计策略采用基于估计的倾向得分曲面的逆概率加权。其中，本项目将对这些复杂的时空背景进行中介分析、效应修正和敏感性分析。本文还将探讨其他重要问题，如最优治疗分配、治疗溢出的空间范围以及持续治疗存在的因果推理。本项目组将以脆弱地区的空袭和经济援助为实证例子。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。pi还将发布软件包，以实现自动化，并将时空框架扩展到其他问题和设置。该项目将提供研究生水平的研究培训机会。该奖项反映了美国国家科学基金会的法定使命，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。