cdgd0_ml: Perform unconditional decomposition via machine learning

In cdgd: Causal Decomposition of Group Disparities

Perform unconditional decomposition via machine learning

Description

Perform unconditional decomposition via machine learning

Usage

cdgd0_ml(Y, D, G, X, data, algorithm, alpha = 0.05, trim = 0, weight = NULL)

Arguments

Y	Outcome. The name of a numeric variable (can be binary and take values of 0 and 1).
D	Treatment status. The name of a binary numeric variable taking values of 0 and 1.
G	Advantaged group membership. The name of a binary numeric variable taking values of 0 and 1.
X	Confounders. A vector of variables names.
data	A data frame.
algorithm	The ML algorithm for modelling. "nnet" for neural network, "ranger" for random forests, "gbm" for generalized boosted models.
alpha	1-alpha confidence interval.
trim	Threshold for trimming the propensity score. When trim=a, individuals with propensity scores lower than a or higher than 1-a will be dropped.
weight	Sampling weights. The name of a numeric variable. If unspecified, equal weights are used. Technically, the weight should be a deterministic function of X and G.

Value

A list of estimates.

Examples

# This example will take a minute to run.

data(exp_data)

set.seed(1)

results <- cdgd0_ml(
Y="outcome",
D="treatment",
G="group_a",
X=c("Q","confounder"),
data=exp_data,
algorithm="gbm")

results[[1]]

cdgd documentation built on June 16, 2025, 9:06 a.m.