https://github.com/gvegayon/blopmatch
blopmatch: Matching Estimator based on a Bilevel Optimization Problem
https://github.com/gvegayon/blopmatch
causal-inference confounding estimator matching nearest-neighbor nonparametric
Last synced: 6 months ago
JSON representation
blopmatch: Matching Estimator based on a Bilevel Optimization Problem
- Host: GitHub
- URL: https://github.com/gvegayon/blopmatch
- Owner: gvegayon
- License: other
- Created: 2017-04-06T07:14:55.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-12-01T07:42:26.000Z (almost 6 years ago)
- Last Synced: 2025-02-14T13:24:34.770Z (8 months ago)
- Topics: causal-inference, confounding, estimator, matching, nearest-neighbor, nonparametric
- Language: R
- Homepage:
- Size: 74.2 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
blopmatch: Matching Estimator based on a Bilevel Optimization Problem
================
[](https://travis-ci.org/gvegayon/blopmatch) [](https://ci.appveyor.com/project/gvegayon/blopmatch) [](https://codecov.io/github/gvegayon/blopmatch?branch=master)
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.
Installation
------------
``` r
devtools::install_github("gvegayon/blopmatch")
```
Examples
--------
``` r
# 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
```