R: Omnibus evaluation of the quality of the random forest...

test_calibration {grf} | R Documentation

## Omnibus evaluation of the quality of the random forest estimates via calibration.

### Description

Test calibration of the forest. Computes the best linear fit of the target estimand using the forest prediction (on held-out data) as well as the mean forest prediction as the sole two regressors. A coefficient of 1 for 'mean.forest.prediction' suggests that the mean forest prediction is correct, whereas a coefficient of 1 for 'differential.forest.prediction' additionally suggests that the forest has captured heterogeneity in the underlying signal. The p-value of the 'differential.forest.prediction' coefficient also acts as an omnibus test for the presence of heterogeneity: If the coefficient is significantly greater than 0, then we can reject the null of no heterogeneity.

### Usage

    test_calibration(forest)

### Arguments

`forest` | The trained forest.

### Value

A heteroskedasticity-consistent test of calibration.

### References

Chernozhukov, Victor, Mert Demirer, Esther Duflo, and Ivan Fernandez-Val. "Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments." arXiv preprint arXiv:1712.04802 (2017).

### Examples

    ## Not run:
    n <- 800
    p <- 5
    X <- matrix(rnorm(n * p), n, p)
    W <- rbinom(n, 1, 0.25 + 0.5 * (X[, 1] > 0))
    Y <- pmax(X[, 1], 0) * W + X[, 2] + pmin(X[, 3], 0) + rnorm(n)
    forest <- causal_forest(X, Y, W)
    test_calibration(forest)

    ## End(Not run)

[Package grf version 1.0.0 Index]