https://github.com/kuan-liu-lab/causens
An R package for causal sensitivity analysis methods
https://github.com/kuan-liu-lab/causens
causal-inference r sensitivity-analysis
Last synced: 12 months ago
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An R package for causal sensitivity analysis methods
- Host: GitHub
- URL: https://github.com/kuan-liu-lab/causens
- Owner: Kuan-Liu-Lab
- License: other
- Created: 2023-09-27T02:21:08.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-03T22:03:41.000Z (about 1 year ago)
- Last Synced: 2025-06-06T19:03:41.237Z (about 1 year ago)
- Topics: causal-inference, r, sensitivity-analysis
- Language: R
- Homepage: https://kuan-liu-lab.github.io/causens/
- Size: 5.95 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# causens 
[](https://github.com/Kuan-Liu-Lab/causens/actions/workflows/R-CMD-check.yaml)
_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._
## Overview
`{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:
- Sensitivity function + propensity score ([Li et al. (2011)](https://pubmed.ncbi.nlm.nih.gov/21659349/), [Brumback et al. (2004)](https://onlinelibrary.wiley.com/doi/10.1002/sim.1657))
- Bayesian parametric sensitivity analysis ([McCandless et Gustafson (2017), Section 2.2](https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7298))
- Monte Carlo sensitivity analysis ([McCandless et Gustafson (2017), Section 2.3](https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7298))
## Installation
``` r
install.packages("devtools")
library(devtools)
devtools::install_github("Kuan-Liu-Lab/causens")
library(causens)
```
## Quickstart
``` r
library(causens)
# Simulate data
data <- simulate_data(N = 10000, seed = 123, alpha_uz = 1,
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
```
## Citing
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/},
}
```
## Getting help or contributing
Please report bugs by opening an
[issue](https://github.com/Kuan-Liu-Lab/causens/issues/new). If you have
a question regarding the usage of `causens`, please open a
[discussion](https://github.com/Kuan-Liu-Lab/causens/discussions/new/choose).
If you would like to contribute to the package, please open a pull request.