Source
cobalt — Noah Greifer
@misc{cobalt,
title = {cobalt},
author = {Noah Greifer},
howpublished = {\url{https://ngreifer.github.io/cobalt/}},
note = {Software / documentation}
}Summary by StatsDoge
Assess covariate balance before/after matching or weighting: standardized mean differences, KS stats, and the publication-ready Love plot.
You're looking at a building block — one of the estimators a
workflow uses inside its pipeline.
You reached it from a workflow step; it's used in 1 workflow (listed below).
https://github.com/ngreifer/cobalt
unpinned — link may rot
⚠️ Unofficial community write-up of cobalt. This account is not affiliated with the authors; it summarizes the public documentation for demonstration. All credit & copyright belong to the original authors.
What it does
After matching or weighting, bal.tab() reports standardized mean differences (and KS statistics) for every covariate, adjusted vs unadjusted; love.plot() turns it into the canonical balance figure with a 0.1 threshold line.
library(cobalt)
bal.tab(W ~ X, weights = w, un = TRUE)
love.plot(W ~ X, weights = w, thresholds = 0.1)
Used in these workflows (1)
-
Covariate balance for matching & weighting (cobalt)
Before you trust an observational estimate, prove balance: SMDs, overlap, and a Love plot before vs after adjustment.
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