Perform Causal Sensitivity Analyses Using Various Statistical Methods


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Documentation for package ‘causens’ version 0.0.3

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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.