Our new ProjectGEOCAUSAL website is live! We’ll be posting the latest updates on cutting-edge applications of spatiotemporal causal inference to address the world’s most pressing problems. Check it out: geocausal.org

We propose a spatiotemporal causal inference framework that fully leverages microlevel, granular data. ATE, heterogeneity, and mediation — all in one framework. Now with updated results and visualizations!

New working paper: “Survey Estimates of Wartime Mortality,” with Gary King, available at gking.harvard.edu/sibs. We provide the first formal proofs of the statistical properties of existing mortality estimators, along with empirical illustrations, to develop intuitions that guide best practices.

Excited to present our poster on spatiotemporal causal inference at #PolMeth 2025. Looking forward to seeing many of you there!

Paper: arxiv.org/abs/2504.03464
Package: github.com/mmukaigawara...

Our PolMeth 2025 poster, which details how we analyze the effects of US airstrikes on insurgent attacks in Iraq using a new geospatial approach and associated R software package ("geocausal")

Interested in causal inference using high-frequency, fine-grained geospatial data? Check out our 2025 PolMeth poster on spatial-temporal causal inference, designed by the wildly talented @mitsurumu.bsky.social. We examine the effects of US airstrikes and civilian harm on insurgent attacks in Iraq

Spatiotemporal causal inference with arbitrary spillover and carryover effects Micro-level data with granular spatial and temporal information are becoming increasingly available to social scientists. Most researchers aggregate such data into a convenient panel data format and a...

New paper alert (hey, I can't doom scroll all the time): This one's on doing causal inference with "microlevel data" where we suspect that the treatment has spatial spillover & temporal carryover effects. We illustrate our new approach + package w/ application to US counterinsurgency efforts in Iraq

Thank you!

Our package, geocausal, implements the proposed methodology. User manual here: osf.io/preprints/os...

Spatiotemporal causal inference with arbitrary spillover and carryover effects Micro-level data with granular spatial and temporal information are becoming increasingly available to social scientists. Most researchers aggregate such data into a convenient panel data format and a...

How can we identify causal effects using micro-level data? Our new framework estimates ATEs, probes causal mechanisms, and uncovers heterogeneity—all in one. We illustrate it with an analysis of airstrikes and insurgent attacks in Iraq. arxiv.org/abs/2504.03464

Here's one example of how we used geocausal to estimate the effects of different distributions of US airstrikes in Iraq on insurgent attacks (open access):

academic.oup.com/jrsssb/artic...

GitHub - mmukaigawara/geocausal: Causal inference with spatio-temporal data in R Causal inference with spatio-temporal data in R. Contribute to mmukaigawara/geocausal development by creating an account on GitHub.

Want to do causal inference using high-frequency geospatial data with temporal carryover or spatial spillover effects? We've created a new R package, geocausal, that lets you estimate counterfactuals at user-specified intervals for different distributions of a treatment

github.com/mmukaigawara...

New in AJPS with @carlynwayne.bsky.social @mitsurumu.bsky.social @profmholmes.bsky.social: how do group dynamics affect assessments of resolve and costly signals? #polisky

onlinelibrary.wiley.com/doi/10.1111/...

GitHub - mmukaigawara/geocausal: Causal inference with spatio-temporal data in R Causal inference with spatio-temporal data in R. Contribute to mmukaigawara/geocausal development by creating an account on GitHub.

Interested in doing causal inference with spatio-temporal data? We've got a new R package, geocausal, that allows you to estimate causal effects + counterfactuals for super fine grained data over user-specified spatial + temporal windows.

Download it here. (Paper TK soon).

polisky