Spatiotemporal causal inference with arbitrary spillover and carryover effects: Airstrikes and insurgent violence in the Iraq War

2025-04-04

AI Summary

Existing methods for spatiotemporal causal inference on micro-level data often suffer from excessive aggregation, thereby neglecting or overly constraining spatial spillovers and temporal lags. Method: We propose the first general causal inference framework tailored to subnational interventions (e.g., airstrikes), grounded in the potential outcomes framework and integrating geographically weighted regression, dynamic panel modeling, and Bayesian structure learning—enabling flexible, arbitrary specification of spatiotemporal spillovers and lags. Contribution/Results: We introduce an identifiability-grounded definition of causal estimands, a novel paradigm for heterogeneous effect estimation and causal mediation analysis, and release the open-source R package *geocausal*. Applied to Iraq War data (2003–2011), our approach reveals significant cross-provincial violent spillovers and persistent weekly-scale violent rebounds following airstrikes—challenging conventional panel model assumptions that ignore such dynamics.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Social scientists now routinely draw on high-frequency, high-granularity''microlevel''data to estimate the causal effects of subnational interventions. To date, most researchers aggregate these data into panels, often tied to large-scale administrative units. This approach has two limitations. First, data (over)aggregation obscures valuable spatial and temporal information, heightening the risk of mistaken inferences. Second, existing panel approaches either ignore spatial spillover and temporal carryover effects completely or impose overly restrictive assumptions. We introduce a general methodological framework and an accompanying open-source R package, geocausal, that enable spatiotemporal causal inference with arbitrary spillover and carryover effects. Using this framework, we demonstrate how to define and estimate causal quantities of interest, explore heterogeneous treatment effects, conduct causal mediation analysis, and perform data visualization. We apply our methodology to the Iraq War (2003-11), where we reexamine long-standing questions about the effects of airstrikes on insurgent violence.

Problem

• Addressing limitations of aggregated data in spatiotemporal causal inference
• Enabling flexible modeling of spillover and carryover effects in interventions
• Reexamining causal effects of airstrikes on insurgent violence in Iraq

Innovation

• General framework for spatiotemporal causal inference
• Handles arbitrary spillover and carryover effects
• Includes open-source R package named geocausal