ce_estimate_rams_att_boot: Causal inference with multiple treatments using RAMS for ATT...

In CIMTx: Causal Inference for Multiple Treatments with a Binary Outcome

Causal inference with multiple treatments using RAMS for ATT effects (bootstrapping for CI)

Description

The function ce_estimate_rams_att_boot implements RAMS with bootstrapping to estimate ATT effect with multiple treatments using observational data.

Usage

ce_estimate_rams_att_boot(
  y,
  w,
  x,
  reference_trt,
  method,
  nboots,
  verbose_boot,
  ...
)

Arguments

y	A numeric vector (0, 1) representing a binary outcome.
w	A numeric vector representing the treatment groups.
x	A dataframe, including all the covariates but not treatments.
reference_trt	A numeric value indicating reference treatment group for ATT effect.
method	A character string. Users can selected from the following methods including "RAMS-Multinomial", "RAMS-GBM", "RAMS-SL".
nboots	A numeric value representing the number of bootstrap samples.
verbose_boot	A logical value indicating whether to print the progress of nonparametric bootstrap.
...	Other parameters that can be passed through to functions.

Value

A summary of the effect estimates can be obtained with summary function.

References

Hadley Wickham (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. URL:https://CRAN.R-project.org/package=stringr

CIMTx documentation built on June 24, 2022, 9:07 a.m.