The package cdgd implements the causal decompositions of group disparities in Yu and Elwert (2025).
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")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 0results0 <- 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 0.1340results1 <- 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
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