cdgd (version 1.0.1)

cdgd0_ml: 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)

Value

A list of estimates.

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.

Examples

# This example will take a minute to run.
# \donttest{
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]]# }