Causal Inference for Multiple Treatments with a Binary Outcome


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Documentation for package ‘CIMTx’ version 1.0.0

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ce_estimate Causal inference with multiple treatments using observational data
ce_estimate_bart_ate Bayesian Additive Regression Trees (BART) for ATE estimation
ce_estimate_bart_att This function implements the BART method when estimand is ATT. Please use our main function ce_estimate.R.
ce_estimate_iptw_ate Inverse probability of treatment weighting (IPTW) for ATE estimation
ce_estimate_iptw_att Inverse probability of treatment weighting (IPTW) for ATT estimation
ce_estimate_rams_ate Regression adjustment with multivariate spline of GPS (RAMS) for ATE estimation
ce_estimate_rams_ate_boot Regression adjustment with multivariate spline of GPS (RAMS) for ATE estimation with bootstrapping
ce_estimate_rams_att Regression adjustment with multivariate spline of GPS (RAMS) for ATT estimation
ce_estimate_ra_ate Regression Adjustment (RA) for ATE estimation
ce_estimate_ra_att Regression Adjustment (RA) for ATT estimation
ce_estimate_tmle_ate Targeted Maximum Likelihood (TMLE) for ATE estimation
ce_estimate_vm_att Vector Macthing (VM) for ATT estimation
covariate_overlap Covariate overlap plot
data_sim Simulate data for binary outcome with multiple treatments
logit Logit function
plot_boxplot Boxplot for weight distribution
plot_contour Contour plot
posterior_summary Summarize posterior samples
sa Flexible Monte Carlo sensitivity analysis for unmeasured confounding
trunc_fun Function to truncate weight