geocausal

The goal of the package geocausal is to implement causal inference analytic methods based on spatio-temporal data. Users provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows.

Installation

You can install the package geocausal from GitHub with:

# install.packages("devtools")
devtools::install_github("mmukaigawara/geocausal")

and CRAN with:

install.packages("geocausal")

Key publications

General methodological framework (ATE, heterogeneity, and mediation):

Mukaigawara M, Imai K, Lyall J, Papadogeorgou G (2025). Spatiotemporal causal inference with arbitrary spillover and carryover effects. arXiv Preprints. April 4. https://arxiv.org/abs/2504.03464

ATE:

Papadogeorgou G, Imai K, Lyall J, and Li F (2022). Causal inference with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq. J R Stat Soc Series B. https://doi.org/10.1111/rssb.12548.

Heterogeneity:

Zhou L, Imai K, Lyall J, Papadogeorgou G (2024). Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference: How Economic Assistance Moderates the Effects of Airstrikes on Insurgent Violence. arXiv Preprints. Dec 19. https://arxiv.org/abs/2412.15128

Please refer to the following preprint for the user guide.

Mukaigawara M, Zhou L, Papadogeorgou G, Lyall J, and Imai K (2024). Geocausal: An R Package for Spatio-temporal Causal Inference. OSF Preprints. December 16. https://doi.org/10.31219/osf.io/5kc6f.

Citation

Please cite this package as follows:

Mukaigawara M, Imai K, Lyall J, Papadogeorgou G (2025). Spatiotemporal causal inference with arbitrary spillover and carryover effects. arXiv Preprints. April 4. https://arxiv.org/abs/2504.03464

Mukaigawara M, Zhou L, Papadogeorgou G, Lyall J, and Imai K (2024). Geocausal: An R Package for Spatio-temporal Causal Inference. OSF Preprints. December 16. https://doi.org/10.31219/osf.io/5kc6f.