README.md

In causens: Perform Causal Sensitivity Analyses Using Various Statistical Methods

causens

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), Brumback et al. (2004))
• Bayesian parametric sensitivity analysis (McCandless et Gustafson (2017), Section 2.2)
• Monte Carlo sensitivity analysis (McCandless et Gustafson (2017), Section 2.3)

Installation

install.packages("devtools")
library(devtools)
devtools::install_github("Kuan-Liu-Lab/causens")
library(causens)

Quickstart

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. 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.

causens documentation built on June 8, 2025, 10:03 a.m.