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sensemakr — Cinelli & Hazlett
@misc{sensemakr,
title = {sensemakr},
author = {Cinelli and Hazlett},
howpublished = {\url{https://github.com/carloscinelli/sensemakr}},
note = {Software / documentation}
}Summary by StatsDoge
How strong would an unobserved confounder have to be to overturn your OLS result? Robustness values + contour plots, no extra assumptions.
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/carloscinelli/sensemakr
unpinned — link may rot
⚠️ Unofficial community write-up of sensemakr. 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
Quantifies how an unobserved confounder would change an OLS estimate: the robustness value (how much residual variance it must explain to nullify the effect), and contour plots benchmarked against observed covariates.
library(sensemakr)
s <- sensemakr(model = fit, treatment = "D",
benchmark_covariates = "X1", kd = 1:3)
plot(s)
Used in these workflows (1)
-
Sensitivity analysis for unobserved confounding (sensemakr)
Don't just assume no unobserved confounding — quantify it: robustness value + contour plots benchmarked against your real covariates.
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