Description
Causal Decomposition of Group Disparities.
Description
The framework of causal decomposition of group disparities developed by Yu and Elwert (2023) <doi:10.48550/arXiv.2306.16591>. This package implements the decomposition estimators that are based on efficient influence functions. For the nuisance functions of the estimators, both parametric and nonparametric options are provided, as well as manual options in case the default models are not satisfying.
README.md
cdgd
The package cdgd implements the causal decompositions of group disparities in Yu and Elwert (2023).
Installation
The latest release of the package can be installed through CRAN.
install.packages("cdgd")
The current development version can be installed from source using devtools.
devtools::install_github("ang-yu/cdgd")
Examples
library(cdgd)
# load the simulated example data
data(exp_data)
head(exp_data)
#> outcome treatment confounder Q group_a
#> 748 1.4608165 1 0.26306864 0.6748330 0
#> 221 0.4777308 0 1.30296394 0.5920512 1
#> 24 0.8760129 1 -1.49971226 1.6294327 1
#> 497 0.4131192 1 -1.17219619 -0.8391873 1
#> 249 2.0483222 1 1.71790879 2.9546966 1
#> 547 0.1912013 0 -0.02438458 -0.3704544 0
Use cdgd0_ml, cdgd0_pa, or cdgd0_manual for unconditional decomposition
results0 <- cdgd0_pa(Y="outcome",D="treatment",G="group_a",X=c("confounder","Q"),data=exp_data,alpha=0.05)
round(results0$results, 4)
#> point se p_value CI_lower CI_upper
#> total 0.2675 0.0390 0.0000 0.1911 0.3439
#> baseline 0.0421 0.0131 0.0013 0.0164 0.0678
#> prevalence 0.2579 0.0337 0.0000 0.1919 0.3240
#> effect -0.1372 0.0209 0.0000 -0.1781 -0.0963
#> selection 0.1047 0.0150 0.0000 0.0754 0.1340
Use cdgd1_ml, cdgd1_pa, or cdgd1_manual for conditional decomposition
results1 <- cdgd1_pa(Y="outcome",D="treatment",G="group_a",X="confounder",Q="Q",data=exp_data,alpha=0.05)
round(results1, 4)
#> point se p_value CI_lower CI_upper
#> total 0.2675 0.0390 0.0000 0.1911 0.3439
#> baseline 0.0421 0.0131 0.0013 0.0164 0.0678
#> conditional prevalence 0.2032 0.0371 0.0000 0.1305 0.2760
#> conditional effect -0.1644 0.0220 0.0000 -0.2076 -0.1212
#> conditional selection 0.0875 0.0143 0.0000 0.0595 0.1156
#> Q distribution 0.0990 0.0188 0.0000 0.0621 0.1359
#> conditional Jackson reduction 0.2362 0.0378 0.0000 0.1621 0.3103