plot_propensities: Plot a histogram of fitted propensities
In ensley-nexant/cfeval: Causal Forest Evaluation and Visualization

Description Usage Arguments Value Examples

The causal forest requires the assumption that we cannot deterministically tell the treatment status of an individual given its covariates. In other words, none of the propensity scores should be near zero or one.

1	plot_propensities(results, ...)

`results`	A trained causal forest object from `causal_forest`
`...`	Additional arguments to be passed to `geom_histogram`

A ggplot2 plot object

## Not run: 
 require(grf)

 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 <- causal_forest(X, Y, W)

 plot_propensities(cf)

## End(Not run)