An implementation of Díaz, Rau and Rivera (2015) matching estimator for causal inference. The authors propose a matching estimator based on a Bilevel Optimization Problem. In raw terms, the two problems are (1) finding a convex combination that (2) using the closets neighbors possible. The solution to this problem allows computing Treatment Effect estimators that significantly improve balance in case-control studies, and furthermore, can be used for data imputation.
devtools::install_github("gvegayon/blopmatch")# Loading the package
library(blopmatch)
# Simulating data
set.seed(1331)
X <- matrix(rnorm(200*5), ncol=5)
# Matching individual 5 to the rest
ans <- blopi_glpk(X[5,,drop=FALSE], X[-5,,drop=FALSE])
# Resulting weights (matches)
ans$lambda
#> 1 x 199 sparse Matrix of class "dgCMatrix"
#>
#> [1,] . . . . . . . . . . . . . . . . . . . . . 0.3544893 . . . . . . . . .
#>
#> [1,] . . . . . . . . . . . . . 0.07906414 . . . . . . . . . . . . . . . .
#>
#> [1,] . . . . . . . . . . . . . . . . . . . . . . . . 0.1294104 . . . . . .
#>
#> [1,] . . . . . . 0.1187391 . . . . . . . . . . . . . 0.2264866 . . . . . .
#>
#> [1,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#>
#> [1,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#>
#> [1,] . . 0.09181041 . . . . . . .
# Target vs Projected
X[5,]
#> [1] -0.1544987 0.8092365 1.0249792 -0.8404142 -1.2016582
ans$lambda %*% X[-5,]
#> 1 x 5 Matrix of class "dgeMatrix"
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] -0.1544987 0.8092365 1.024979 -0.8404142 -1.201658