README.md
In ensley-nexant/cfeval: Causal Forest Evaluation and Visualization

cfeval

In the course of an analysis, many causal forest models may need to be created, evaluated, and compared to one another. The cfeval package aims to streamline that process by providing utility and plotting functions to perform repetitive tasks common to all of these models.

cfeval requires the grf package to function, and is heavily dependent on its API.

You can install the latest version of cfeval from Github with

devtools::install_github('ensley-nexant/cfeval')

or, if you have located the path to the source tarball, with

install.packages('E:\Path\To\Package\cfeval_x.x.x.tar.gz', repos = NULL, type = 'source')

First create an example dataset and fit a causal forest.

library(cfeval)

n <- 2000; p <- 10

X <- matrix(rnorm(n * p), n, p)
W <- rbinom(n, 1, 0.4 + 0.2 * (X[, 1] > 0))
Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)

cf <- grf::causal_forest(X, Y, W)

Next, create a causal forest evaluation object, passing the trained forest and the original training dataset.

cfe <- cf_eval(cf, X)

The cf_eval object contains three elements:

dat, the original training dataset;
res, summarized results from out-of-bag training predictions;
varimp, all covariates used in the forest along with their relative importances.

Note that the trained forest cf, which can be several gigabytes in size even for moderately sized problems, is not copied or carried around by cf_eval. This improves performance. For actual work, I recommend compressing the forest and writing it to disk with saveRDS(), then loading it for evaluation when required.

One of the many plotting convenience functions offered by cfeval is to view the distribution of estimated conditional average treatment effects.