Perform Causal Sensitivity Analyses Using Various Statistical Methods

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Documentation for package ‘causens’ version 0.0.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

bayesian_causens	Bayesian parametric sensitivity analysis for causal inference
causens_monte_carlo	Monte Carlo sensitivity analysis for causal effects
causens_sf	Bayesian Estimation of ATE Subject to Unmeasured Confounding
create_jags_model	Create an JAGS model for Bayesian sensitivity analysis
gData_U_binary_Y_binary	Generate data with a binary unmeasured confounder and binary outcome
gData_U_binary_Y_cont	Generate data with a binary unmeasured confounder and continuous outcome
gData_U_cont_Y_binary	Generate data with a continuous unmeasured confounder and a binary outcome
gData_U_cont_Y_cont	Generate data with a continuous unmeasured confounder and continuous outcome
plot_causens	Plot ATE with respect to sensitivity function value when it is constant, i.e. c(1, e) = c1 and c(0, e) = c0.
process_model_formula	Process model formula
sf	Calculate sensitivity of treatment effect estimate to unmeasured confounding
simulate_data	Generate data with unmeasured confounder
summary.bayesian_causens	Summarize the results of a causal sensitivity analysis via Bayesian modelling of an unmeasured confounder.
summary.causens_sf	Summarize the results of a causal sensitivity analysis via sensitivity function.
summary.monte_carlo_causens	Summarize the results of a causal sensitivity analysis via the Monte Carlo method.