HDCATE: High-Dimensional Conditional Average Treatment Effects...

In hdcate: Estimation of Conditional Average Treatment Effects with High-Dimensional Data

High-Dimensional Conditional Average Treatment Effects (HDCATE) Estimator

Description

Use a two-step procedure to estimate the conditional average treatment effects (CATE) with potentially high-dimensional covariate(s). Run browseVignettes('hdcate') to browse the user manual of this package.

Usage

HDCATE(data, y_name, d_name, x_formula)

Arguments

data	data frame of the observed data
y_name	variable name of the observed outcomes
d_name	variable name of the treatment indicators
x_formula	formula of the covariates

Value

An initialized HDCATE model (object), ready for estimation.

Examples

# get simulation data
n_obs <- 500  # Num of observations
n_var <- 100  # Num of observed variables
n_rel_var <- 4  # Num of relevant variables
data <- HDCATE.get_sim_data(n_obs, n_var, n_rel_var)
# conditional expectation model is misspecified
x_formula <- paste(paste0('X', c(2:n_var)), collapse ='+')
# for example, and alternatively, the propensity score model is misspecified
# x_formula <- paste(paste0('X', c(1:(n_var-1))), collapse ='+')

# Example 1: full-sample estimator
# create a new HDCATE model
model <- HDCATE(data=data, y_name='Y', d_name='D', x_formula=x_formula)

# estimate HDCATE function, inference, and plot
HDCATE.set_condition_var(model, 'X2', min=-1, max=1, step=0.01)

HDCATE.fit(model)
HDCATE.inference(model)
HDCATE.plot(model)


# Example 2: cross-fitting estimator
# change above estimator to cross-fitting mode, 5 folds, for example.
HDCATE.use_cross_fitting(model, k_fold=5)

# estimate HDCATE function, inference, and plot
HDCATE.set_condition_var(model, 'X2', min=-1, max=1, step=0.01)

HDCATE.fit(model)
HDCATE.inference(model)
HDCATE.plot(model)

hdcate documentation built on Dec. 16, 2022, 5:19 p.m.