Publications

Peer-Reviewed Publications

2022

Causal Inference with Spatio-Temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq
Georgia Papadogeorgou, Kosuke Imai, Jason Lyall, and Fan Li.
Journal of the Royal Statistical Society Series B: Statistical Methodology.
2022;84(5):1969–1999.
PDF

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.

BibTeX

@article{papadogeorgou2022causal,
  title={Causal Inference with Spatio-Temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq},
  author={Papadogeorgou, Georgia and Imai, Kosuke and Lyall, Jason and Li, Fan},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={84},
  number={5},
  pages={1969--1999},
  year={2022},
  publisher={Oxford University Press}
}

Working Papers

2025

Spatiotemporal Causal Inference with Arbitrary Spillover and Carryover Effects: Airstrikes and Insurgent Violence in the Iraq War
Mitsuru Mukaigawara, Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou.
PDF

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.

BibTeX

@article{mukaigawara2025spatiotemporal,
  title={Spatiotemporal Causal Inference with Arbitrary Spillover and Carryover Effects: Airstrikes and Insurgent Violence in the Iraq War},
  author={Mukaigawara, Mitsuru and Imai, Kosuke and Lyall, Jason and Papadogeorgou, Georgia},
  journal={arXiv preprint arXiv:2504.03464},
  year={2025}
}

2024

Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference
Lingxiao Zhou, Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou.
PDF

Abstract

Scholars from diverse fields increasingly rely on high-frequency spatio-temporal data. Yet, causal inference with these data remains challenging due to spatial spillover and temporal carryover effects. We develop methods to estimate heterogeneous treatment effects by allowing for arbitrary spatial and temporal causal dependencies. We focus on common settings where the treatment and outcomes are time-varying spatial point patterns and where moderators are either spatial or spatio-temporal variables. We define causal estimands based on stochastic interventions where researchers specify counterfactual distributions of treatment events. We propose the Hajek-type estimator of the conditional average treatment effect (CATE) as a function of spatio-temporal moderator variables, and establish its asymptotic normality as the number of time periods increases. We then introduce a statistical test of no heterogeneous treatment effects. Through simulations, we evaluate the finite-sample performance of the proposed CATE estimator and its inferential properties. Our motivating application examines the heterogeneous effects of US airstrikes on insurgent violence in Iraq. Drawing on declassified spatio-temporal data, we examine how prior aid distributions moderate airstrike effects. Contrary to expectations from counterinsurgency theories, we find that prior aid distribution, along with greater amounts of aid per capita, is associated with increased insurgent attacks following airstrikes.

BibTeX

@article{zhou2024estimating,
  title={Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference},
  author={Zhou, Lingxiao and Imai, Kosuke and Lyall, Jason and Papadogeorgou, Georgia},
  journal={arXiv preprint arXiv:2412.15128},
  year={2024}
}

geocausal: An R Package for Spatio-Temporal Causal Inference
Mitsuru Mukaigawara, Lingxiao Zhou, Georgia Papadogeorgou, Jason Lyall, and Kosuke Imai.
PDF

Abstract

Scholars from diverse fields now use highly disaggregated (“microlevel”) data with fine-grained spatial (e.g., locations of villages and individuals) and temporal (days, hours, or even seconds) dimensions to test their theories. Despite the proliferation of these data, however, statistical methods for causal inference with spatio-temporal data remain underdeveloped. We introduce an R package, geocausal, that enables researchers to implement causal inference methods for highly disaggregated spatio-temporal data. The geocausal package implements two necessary steps for spatio-temporal causal inference: (1) preparing the data and (2) estimating causal effects. The geocausal package allows users to effectively use fine-grained spatio-temporal data, test counterfactual scenarios that have spatial and temporal dimensions, and visualize each step efficiently. We illustrate the capabilities of the geocausal package by analyzing the US airstrikes and insurgent attacks in Iraq over various spatial and temporal windows.

BibTeX

@article{mukaigawara2024geocausal,
  title={geocausal: An R Package for Spatio-Temporal Causal Inference},
  author={Mukaigawara, Mitsuru and Zhou, Lingxiao and Papadogeorgou, Georgia and Lyall, Jason and Imai, Kosuke},
  journal={OSF Preprints},
  year={2024},
  doi={10.31219/osf.io/5kc6f}
}