MLbalance (alpha version)

MLbalance implements a novel machine learning balance test, the balance permutation test, for experiments with binary, multiarm, and continuous treatments. The purpose of this test is to detect failures of random assignment and imbalance across treatment arms. For more detail, see Rametta and Fuller (2023). This package is in alpha, any recommendations or comments welcome in the issues section.

Installation

You can install the development version of MLbalance from GitHub with:

# install.packages("devtools")
devtools::install_github("CetiAlphaFive/MLbalance")
# OR 
# install.packages("remotes)
remotes::install_github("CetiAlphaFive/MLbalance")
# OR 
# install.packages("pak")
pak::pak("CetiAlphaFive/MLbalance")

Future stable versions will be available on CRAN and also on Github here:

devtools::install_github("samjfuller/MLbalance)

Binary Treatment Example

Here is a basic example demonstrating the balance permutation test on a simulated binary treatment DGP with multidimensional contamation of the treatment assignment.

# install.packages("randomizr")
library(MLbalance)
#
set.seed(1995)
#
# Simple simulation 
n <- 1000
p <- 20
X <- matrix(rnorm(n*p,0,1),n,p)
w_real <- rbinom(n, 1, ifelse(.021 + abs(.4*X[,4] - .5*X[,8]) < 1, .021 + abs(.4*X[,4] - .5*X[,8]), 1))
# install.packages("randomizr")
w_sim <- randomizr::complete_ra(N = n,m = sum(w_real))
e <- rnorm(n,0,1)
y <- 2*w_real*X[,4] + 3*X[,2] -2*X[,8] + e
df <- data.frame(y,w_real,w_sim,X)
#
r.check <- random_check(W_real = df$w_real, #real treatment assignment vector 
                        W_sim  = df$w_sim, #simulated vector, comment out this argument to use permutated real assignment vector instead 
                        X      = subset(df,select = -c(y,w_real,w_sim)) #matrix of pretreatment covariates (or any covariates that SHOULD NOT be related to the assignment process/mechanism
             ); r.check$plot
#> Simulated Assignemnt Vector Provided, Null Distribution Generated Using Simulated Treatment Assignment.
#> 
#> 
#> Simple Count Table(s)
#> 
#> W_real
#>   0   1 
#> 520 480 
#> W_sim
#>   0   1 
#> 520 480 
#> 
#> 
#> Result from difference in variances test (one-sided, greater F-test):
#> 
#>  Statistic p.val Result
#>   139.8141     0   FAIL
#> 
#> 
#> Check diff.var.result in saved output for detailed test result.

# to see variables important for predicting assignment, check r.check$imp.predictors