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Why is it that more shark attacks occur when more ice cream is sold? The answer: both are related to the weather, here an unmeasured confounder.
{causens}
is an R package that will allow to perform
various sensitivity analysis methods to adjust for unmeasured
confounding within the context of causal inference. Currently, we
provide the following methods:
install.packages("devtools")
library(devtools)
::install_github("Kuan-Liu-Lab/causens")
devtoolslibrary(causens)
library(causens)
# Simulate data
<- simulate_data(N = 10000, seed = 123, alpha_uz = 1,
data beta_uy = 1, treatment_effects = 1)
# Treatment model is incorrect since U is "missing"
causens_sf(Z ~ X.1 + X.2 + X.3, "Y", data = data, c1 = 0.25, c0 = 0.25)$estimated_ate
Please cite our software using:
@Manual{,
title = {causens: Perform Causal Sensitivity Analyses Using Various Statistical Methods},
author = {Larry Dong and Yushu Zou and Kuan Liu},
year = {2024},
note = {R package version 0.0.3, https://github.com/Kuan-Liu-Lab/causens},
url = {https://kuan-liu-lab.github.io/causens/},
}
Please report bugs by opening an issue. If
you have a question regarding the usage of causens
, please
open a discussion.
If you would like to contribute to the package, please open a pull
request.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.