dimw: Difference in Means and Difference in Weighted Means

In kbal: Kernel Balancing

Difference in Means and Difference in Weighted Means

Description

Calculates the simple difference in means or weighted difference in means between the control or sample population and the treated or target population.

Usage

dimw(X, w, target)

Arguments

X	matrix of data where rows are observations and columns are covariates.
w	numeric vector of weights for each observation.
target	numeric vector of length equal to the total number of units where population/treated units take a value of 1 and sample/control units take a value of 0.

Value

dim	the simple, unweighted difference in means.
dimw	the weighted difference in means.

Examples

#let's say we want to get the unweighted DIM and the weighted DIM using weights from the kbal
#function with the lalonde data:
#load and clean data a bit
set.seed(123)
data("lalonde")
# Select a random subset of 500 rows
lalonde_sample <- sample(1:nrow(lalonde), 500, replace = FALSE)
lalonde <- lalonde[lalonde_sample, ]

xvars=c("age","black","educ","hisp","married","re74","re75","nodegr","u74","u75")

#get the kbal weights
kbalout= kbal(allx=lalonde[,xvars],
               sampledinpop=FALSE,
               treatment=lalonde$nsw)
 #now use dimw to get the DIMs
 dimw(X = lalonde[,xvars], w = kbalout$w, target = lalonde$nsw)

kbal documentation built on April 3, 2025, 6:04 p.m.