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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 Hájek 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 1 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 km away.
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Policy effect evaluation under counterfactual neighbourhood intervention in the presence of spillover
Abstract Policy interventions can spill over to units of a population that is not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighbouring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our research question broadens this scope by asking what policy consequences would the treated units have experienced under counterfactual exposure settings. When we only observe treated unit(s) surrounded by controls—as is common when a policy intervention is implemented in a single city or state—this effect inquires about the policy effects under a counterfactual neighbourhood policy status that we do not, in actuality, observe. In this work, we extend difference-in-differences approaches to spillover settings and develop identification conditions required to evaluate policy effects in counterfactual treatment scenarios. These causal quantities are policy-relevant for designing effective policies for populations subject to various neighbourhood statuses. We develop several estimators that have desirable properties. We provide an illustrative data application to the Philadelphia beverage tax study.
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- Award ID(s):
- 2149716
- PAR ID:
- 10571719
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series A: Statistics in Society
- ISSN:
- 0964-1998
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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