cf_eval: Create a causal forest evaluation object
In ensley-nexant/cfeval: Causal Forest Evaluation and Visualization

Description Usage Arguments Value Examples

Create a causal forest evaluation object

1	cf_eval(fit, Xdat, preds = NULL)

`fit`	A trained `causal_forest` object
`Xdat`	The data frame `fit` was trained on. It may include categorical covariates.
`preds`	Output from `predict.causal_forest`. If omitted, it will be called directly to generate OOB predictions and corresponding variance estimates. This may be time consuming.

A cf_eval object, which is essentially just a list with the following elements:

Xdat: The same as the input Xdat but converted to a tibble
res: A results object
varimp: A varimp (variable importance) object

## Not run: 
 require(grf)

 Xdat <- subset(cfex, select = -c(W, Y))
 X <- make_contrasts(Xdat, 'fct')
 cf <- causal_forest(X, cfex$Y, cfex$W)

 cf_eval(cf, Xdat)

## End(Not run)