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)

- A
Exposure (binary integer: 0 or 1)

- verbose
Whether to print the result (logical; Default = FALSE)

- W
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`

.

- W.Q
Only valid when `W`

is null, otherwise it would be replaced by `W`

.
Covariates for outcome model (vector, matrix or data.frame).

- W.g
Only valid when `W`

is null, otherwise it would be replaced by `W`

.
Covariates for exposure model (vector, matrix or data.frame)

- Q.SL.library
SuperLearner libraries or sl3 learner object (Lrnr_base) for outcome model

- g.SL.library
SuperLearner libraries or sl3 learner object (Lrnr_base) for exposure model

- k_split
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.

- g.bound
Value between [0,1] at which the propensity score should be truncated. Defaults to 0.025.

- stratified_fit
An indicator for whether the outcome model is fitted stratified by exposure status in the`fit()`

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

## Examples

```
library(SuperLearner)
aipw_sl <- aipw_wrapper(Y=rbinom(100,1,0.5), A=rbinom(100,1,0.5),
W.Q=rbinom(100,1,0.5), W.g=rbinom(100,1,0.5),
Q.SL.library="SL.mean",g.SL.library="SL.mean",
k_split=1,verbose=FALSE)
```