geocausal: An R Package for Spatio-Temporal Causal Inference

Mitsuru Mukaigawara, Lingxiao Zhou, Georgia Papadogeorgou, Jason Lyall and Kosuke Imai
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Mitsuru Mukaigawara: Harvard University
Jason Lyall: Dartmouth College

No 5kc6f, OSF Preprints from Center for Open Science

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.

Date: 2024-08-27
New Economics Papers: this item is included in nep-geo
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:5kc6f

DOI: 10.31219/osf.io/5kc6f

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