Package: spaci 0.2.0

spaci: Causal Effect Estimation Under Spatial Confounding and Interference

Implements the distance-adjusted propensity score with interference (iDAPS) and recoverU+ methods for estimating the average treatment effect on the treated (ATT) from spatial observational data in the presence of both spatial confounding and spatial interference. iDAPS matches units on a data-driven composite of propensity-score distance, spatial proximity and neighbourhood-exposure distance. recoverU+ is a doubly robust estimator that augments the propensity-score and control-outcome models with a partially recovered spatial confounder and a neighbourhood-exposure term. The package also provides the naive propensity score, DAPS and recoverU comparators, a simulator for the spatial confounding/interference data-generating process and spatial inference tools

Authors:Isqeel Ogunsola [aut, cre], Olatunji Johnson [aut], Thomas House [aut]

spaci_0.2.0.tar.gz
spaci_0.2.0.zip(r-4.7-any)spaci_0.2.0.zip(r-4.6-any)spaci_0.2.0.zip(r-4.5-any)
spaci_0.2.0.tgz(r-4.6-any)spaci_0.2.0.tgz(r-4.5-any)
spaci_0.2.0.tar.gz(r-4.7-any)spaci_0.2.0.tar.gz(r-4.6-any)
spaci_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spaci/json (API)

# Install 'spaci' in R:
install.packages('spaci', repos = c('https://ogunsolaia.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ogunsolaia/spaci/issues

On CRAN:

Conda:

4.78 score 9 scripts 231 downloads 13 exports 0 dependencies

Last updated from:6f91cdfb67. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE141
source / vignettesOK208
linux-releaseNOTE144
macos-releaseNOTE114
macos-oldrelNOTE139
windows-develNOTE112
windows-releaseNOTE98
windows-oldrelNOTE111
wasm-releaseOK89

Exports:bias_boundboot_spatialdapsidapsnaive_psneighbourhood_exposureplot_aterand_testrecoverUrecoverUplussimulate_spatial_causalspatial_atevcov_hac

Dependencies:

Application: does SCR/SNCR technology reduce ambient ozone?
Background | Loading and preparing the data | A note on units | Estimating the effect with every method | Why DAPS and iDAPS need a seed | Conclusion | References
Rendered fromozone-application.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05

Estimating causal effects under spatial confounding and interference
The problem | Simulate data | Estimate the ATT with every method | Visualising the comparison | iDAPS in detail | recoverU+ in detail | A small simulation study
Rendered fromspaci.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-10-05