Omnibus evaluation of the quality of the random forest estimates via calibration.
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.
test_calibration(forest)
forest |
The trained forest. |
A heteroskedasticity-consistent test of calibration.
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).
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)
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