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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 conducted to examine the
estimators' finite sample performance. Finally, we illustrate the
proposed methods by estimating the effects of American airstrikes on
insurgent violence in Iraq from February 2007 to July 2008. Our
analysis suggests that increasing the average number of daily
airstrikes for up to one month may result in more insurgent
attacks. We also find some evidence that airstrikes can displace
attacks from Baghdad to new locations up to 400 kilometers
away. |
Zhou, Lingxiao, and Kosuke Imai, Jason
Lyall, and Georgia Papadogeorgou. ``Estimating Heterogeneous
Treatment Effects for Spatio-Temporal Causal Inference: How
Economic Assistance Moderates the Effects of Airstrikes on
Insurgent Violence.'' |
Mukaigawara, Mitsuru, and Kosuke Imai,
Jason Lyall, and Georgia Papadogeorgou. ``Spatiotemporal causal inference with
arbitrary spillover and carryover
effects.'' |
Mukaigawara, Mitsuru, Georgia Papadogeorgou,
Jason Lyall, and Kosuke Imai. ``geocausal:
Causal Inference with Spatio-Temporal Data.''
available through The
Comprehensive R Archive Network and GitHub.
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