Calibration test -- description and source code

[lucy-temed]

Hi everyone,
Thank you for posting this package.
I am trying to understand the intuition of the calibration test.
I see from the source code that the calibration test does the following for a causal forest:

preds <- predict(forest)$predictions
mean.pred <- mean(preds)
DF <- data.frame(
target = unname(forest$Y.orig - forest$Y.hat),
mean.forest.prediction = unname(forest$W.orig - forest$W.hat) * mean.pred,differential.forest.prediction = unname(forest$W.orig - forest$W.hat) *(preds - mean.pred))

summary(lm(target~ mean.forest.prediction + differential.forest.prediction +0, data=DF))

The target are the orthogonalized outcomes. But then, the target is not regressed on the mean forest prediction and the differential forest prediction alone, but on the product of those two and the orthogonalized treatments....

I understand that those orthogonalized outcomes are the outcome variable of the forest. But i don't understand why the mean forest prediction needs to be multiplied by the orthogonalized treatments for the test to work.

I am just so curious why the description of the function says that the test computes the best linear predictor of the target estimand using the forest prediction as well as the mean forest prediction as the sole two regressors. It seems to me that the test uses the forest prediction and the mean forest prediction multiplied by the orthogonalized treatment status as the sole two regressors.

Or is this clarification redundant?

Any guidance on this would be greatly appreciated.

Lucy

[erikcs]

Hi @lucy-temed, see if answer in #712 helps you out. (PS: a new feature that could subsume this exercise is the RATE).