Type:	Package
Title:	Perform Causal Sensitivity Analyses Using Various Statistical Methods
Version:	0.0.3
Description:	While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In 'causens', three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).
License:	MIT + file LICENSE
URL:	https://kuan-liu-lab.github.io/causens/, https://github.com/Kuan-Liu-Lab/causens
Suggests:	devtools, knitr, pkgdown, PSweight, rjags, rmarkdown, roxygen2, testthat (≥ 3.0.0), usethis, waldo
Encoding:	UTF-8
RoxygenNote:	7.3.2
BugReports:	https://github.com/Kuan-Liu-Lab/causens/issues
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-06-03 22:00:19 UTC; larryshamalama
Author:	Larry Dong [aut, cre], Yushu Zou [aut], Kuan Liu [aut]
Maintainer:	Larry Dong <larry.dong@mail.utoronto.ca>
Repository:	CRAN
Date/Publication:	2025-06-05 11:20:06 UTC

Bayesian parametric sensitivity analysis for causal inference

Description

This function runs a Bayesian sensitivity analysis for causal inference using JAGS or Stan as a backend. For now, only JAGS is supported.

Usage

bayesian_causens(
  trt_model,
  outcome_model,
  U_model,
  data,
  beta_uy = ~dunif(-2, 2),
  alpha_uz = ~dunif(-2, 2),
  backend = "jags",
  output_trace = FALSE,
  ...
)

Arguments

Value

A list of posterior samples for the causal effect of the exposure variable on the outcome, as well as the confounder-adjusted causal effect.

Monte Carlo sensitivity analysis for causal effects

Description

This function performs a Monte Carlo sensitivity analysis for causal effects.

Usage

causens_monte_carlo(outcome, exposure, confounders, data, ...)

Arguments

Value

The estimated causal effect.

Bayesian Estimation of ATE Subject to Unmeasured Confounding

Description

This function provides an estimate of the Average Treatment Effect (ATE) using Bayesian modelling.

Usage

causens_sf(
  trt_model,
  outcome,
  data,
  bootstrap = FALSE,
  B = 1000,
  seed = 123,
  ...
)

Arguments

Value

A point estimate of the corrected ATE.

Create an JAGS model for Bayesian sensitivity analysis

Description

Creates a JAGS model available as a string, or .txt file, where priors are initialized to be uninformative by default.

Usage

create_jags_model(binary_outcome, beta_uy, alpha_uz)

Arguments

Generate data with a binary unmeasured confounder and binary outcome

Description

Generate data with a binary unmeasured confounder and binary outcome

Usage

gData_U_binary_Y_binary(
  ymodel = "linear",
  N = 500,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  seed = 123
)

Arguments

Value

A data frame with the simulated dataset with U binary, Y binary.

Generate data with a binary unmeasured confounder and continuous outcome

Description

Generate data with a binary unmeasured confounder and continuous outcome

Usage

gData_U_binary_Y_cont(
  ymodel = "linear",
  N = 500,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  seed = 123
)

Arguments

Value

A data frame with the simulated dataset with U binary, Y continuous.

Examples

fulldata <- gData_U_binary_Y_cont(
  ymodel = "linear",
  N = 500,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  seed = 123
)
table(fulldata$Z)

Generate data with a continuous unmeasured confounder and a binary outcome

Description

Generate data with a continuous unmeasured confounder and a binary outcome

Usage

gData_U_cont_Y_binary(
  ymodel = "linear",
  N = 500,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  seed = 123
)

Arguments

Value

A data frame with the simulated dataset with U continuous, Y binary.

Generate data with a continuous unmeasured confounder and continuous outcome

Description

Generate data with a continuous unmeasured confounder and continuous outcome

Usage

gData_U_cont_Y_cont(
  ymodel = "linear",
  N = 500,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  seed = 123
)

Arguments

Value

A data frame with the simulated dataset with U continuous, Y continuous.

Plot ATE with respect to sensitivity function value when it is constant, i.e. c(1, e) = c1 and c(0, e) = c0.

Description

This function plots 1) the ATE as a function of the sensitivity function value when it is constant and 2) its associate 95 interval obtained via bootstrapping, if desired. The latter process can be take a few seconds to minutes.

Usage

plot_causens(
  trt_model,
  data,
  outcome,
  c1_upper = 0.5,
  c1_lower = 0,
  r = 1,
  by = 0.01
)

Arguments

Value

A plot of the ATE as a function of c1 values.

Process model formula

Description

This helper function takes in a model in its 'formula' format and returns a list comprising of various information, including the fitted model as an R object.

Usage

process_model_formula(model, data)

Arguments

Value

A list containing the fitted model, model formula, response variable name, and confounder names.

Calculate sensitivity of treatment effect estimate to unmeasured confounding

Description

This function calculates the sensitivity of a treatment effect estimate to unmeasured confounding, as described in Rosenbaum (2002). The sensitivity is defined as the maximum strength of association between the unmeasured confounder and the treatment assignment that would be needed to explain away the observed treatment effect estimate. This function assumes that the treatment assignment is binary and that the outcome is continuous.

Usage

sf(z, e, form = "constant", c1 = 0, c0 = 0, s1 = 0, s0 = 0)

Arguments

Value

Sensitivity of treatment effect estimate to unmeasured confounding (numeric)

sf(z, e, form)

Generate data with unmeasured confounder

Description

Generate data with unmeasured confounder

Usage

simulate_data(
  ymodel = "linear",
  N = 500,
  u_type = "binary",
  y_type = "continuous",
  seed = 123,
  alpha_uz = 0.2,
  beta_uy = 0.5,
  treatment_effects = 1,
  informative_u = FALSE
)

Arguments

Value

A data frame with the simulated dataset.

Summarize the results of a causal sensitivity analysis via Bayesian modelling of an unmeasured confounder.

Description

Summarize the results of a causal sensitivity analysis via Bayesian modelling of an unmeasured confounder.

Usage

## S3 method for class 'bayesian_causens'
summary(object, ...)

Arguments

Value

A summary of results from a Bayesian causal sensitivity analysis.

Summarize the results of a causal sensitivity analysis via sensitivity function.

Description

Summarize the results of a causal sensitivity analysis via sensitivity function.

Usage

## S3 method for class 'causens_sf'
summary(object, ...)

Arguments

Value

A summary of the results of the causal sensitivity analysis.

Summarize the results of a causal sensitivity analysis via the Monte Carlo method.

Description

Summarize the results of a causal sensitivity analysis via the Monte Carlo method.

Usage

## S3 method for class 'monte_carlo_causens'
summary(object, ...)

Arguments

Value

A summary of results from a Monte Carlo causal sensitivity analysis.