Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq
Georgia Papadogeorgou, Kosuke Imai, Jason Lyall, Fan Li

TL;DR
This paper develops a novel causal inference framework for spatio-temporal point processes, enabling estimation of treatment effects such as airstrikes on insurgent violence, accounting for spillover and carryover effects.
Contribution
It extends the potential outcomes framework to spatio-temporal point processes and introduces estimators with proven consistency and asymptotic normality, including sensitivity analysis methods.
Findings
Increasing airstrikes may lead to more insurgent attacks.
Airstrikes can displace violence to locations up to 400 km away.
The proposed estimators perform well in finite samples.
Abstract
Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular stochastic treatment assignment strategy. Our methodology allows for arbitrary patterns of spatial spillover and temporal carryover effects. Using martingale theory, we show that the proposed estimator is consistent and asymptotically normal as the number of time periods increases. We propose a sensitivity analysis for the possible existence of unmeasured confounders, and extend it to the Hajek estimator. Simulation studies are…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Statistical Methods and Bayesian Inference
