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Description

Causal Inference with Spatio-Temporal Data.

Spatio-temporal causal inference based on point process data. You 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. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548>.

geocausal

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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.

For methodological details, please refer to the following article.

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.

Additionally, an article describing the usage of this package is coming soon.

Citation

Please cite this package as follows:

Mukaigawara M, Zhou L, Papadogeorgou G, Lyall J, Imai K (2024). geocausal: Causal Inference with Spatio-Temporal Data. R package version 0.3.1, https://github.com/mmukaigawara/geocausal

Installation

You can install the package geocausal from GitHub with:

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

and CRAN with:

install.packages("geocausal")
Metadata

Version

0.3.2

License

Unknown

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