compute_ipw: Estimation of Inverse Probability Weighting

In ygeunkim/propensityml: Propensity score weighting using machine learning methods

Description

estimates inverse probability weighting (IPW) based on propensity score estimates

Usage

compute_ipw(
  data,
  treatment,
  trt_indicator = 1,
  outcome,
  weight = NULL,
  object = NULL,
  formula = NULL,
  method = c("logit", "rf", "cart", "SVM"),
  mc_col = NULL,
  sc_col = NULL,
  parallel = FALSE,
  ...
)

Arguments

data	A data frame to be used
treatment	Treatment variable name
trt_indicator	Value that indicates the unit is treated
outcome	Outcome variable name
weight	If weighting column exists, it can be specified for computing efficiency
object	A propmod object if already fitted.
formula	If not, write a formula to be fitted. Remember that you don't have to worry about group variable. .SD do exclude by.
method	Estimating methods "logit" - ps_glm "rf" - ps_rf "cart" - ps_cart "SVM" - ps_svm
mc_col	Indicator column name for MC simulation if exists
sc_col	Indicator column name for various scenarios if exists
parallel	parallelize some operation
...	Additional arguments of fitting functions

ygeunkim/propensityml documentation built on Jan. 1, 2021, 1:44 p.m.