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(Z ~ X.1 + X.2 + X.3, "Y", data = data, method = "sf", c1 = 0.25, c0 = 0.25)

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 0.0.2-9000, 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.