A wrapper function for AIPW$new()$fit()\$summary()

aipw_wrapper(
Y,
A,
verbose = TRUE,
W = NULL,
W.Q = NULL,
W.g = NULL,
Q.SL.library,
g.SL.library,
k_split = 10,
g.bound = 0.025,
stratified_fit = FALSE
)

## Arguments

Y Outcome (binary integer: 0 or 1) Exposure (binary integer: 0 or 1) Whether to print the result (logical; Default = FALSE) covariates for both exposure and outcome models (vector, matrix or data.frame). If null, this function will seek for inputs from W.Q and W.g. Only valid when W is null, otherwise it would be replaced by W. Covariates for outcome model (vector, matrix or data.frame). Only valid when W is null, otherwise it would be replaced by W. Covariates for exposure model (vector, matrix or data.frame) SuperLearner libraries or sl3 learner object (Lrnr_base) for outcome model SuperLearner libraries or sl3 learner object (Lrnr_base) for exposure model Number of splitting (integer; range: from 1 to number of observation-1): if k_split=1, no cross-fitting; if k_split>=2, cross-fitting is used (e.g., k_split=10, use 9/10 of the data to estimate and the remaining 1/10 leftover to predict). NOTE: it's recommended to use cross-fitting. Value between [0,1] at which the propensity score should be truncated. Defaults to 0.025. An indicator for whether the outcome model is fitted stratified by exposure status in thefit() method. Only when using stratified_fit() to turn on stratified_fit = TRUE, summary outputs average treatment effects among the treated and the controls.

## Value

A fitted AIPW object with summarised results