ce_estimate_bart_att: Causal inference with multiple treatments using BART for ATT...

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

Causal inference with multiple treatments using BART for ATT effects

Description

The function ce_estimate_bart_att implements BART to estimate ATT effect with multiple treatments using observational data.

Usage

ce_estimate_bart_att(
  y,
  x,
  w,
  discard = FALSE,
  ndpost = 1000,
  reference_trt,
  ...
)

Arguments

y	A numeric vector (0, 1) representing a binary outcome.
x	A dataframe, including all the covariates but not treatments.
w	A numeric vector representing the treatment groups.
discard	A logical indicating whether to use the discarding rules. The default is FALSE.
ndpost	A numeric value indicating the number of posterior draws.
reference_trt	A numeric value indicating reference treatment group for ATT effect.
...	Other parameters that can be passed through to functions.

Value

A summary of the effect estimates can be obtained with summary function. The output also contains a list of the posterior samples of causal estimands. When discard = TRUE, the output contains number of discarded individuals.

References

Sparapani R, Spanbauer C, McCulloch R Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package. Journal of Statistical Software, 97(1), 1-66.

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7. URL: https://CRAN.R-project.org/package=dplyr

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