Evaluating a policy tree in an observational setting #142
Description
In this tutorial, it is described how one can evaluate a policy tree which is generated in an experimental setting:
# Only valid for experimental setting!
# predicting pi(X[i]) for each value in the test set
# policytree labels each treatment as 1,2,3... Here, we subtract one so that zero represents control, as we're used to in the case of a binary treatment.
w.opt <- predict(policy, X[-train,]) - 1 A <- w.opt == 1
# Copied and pasted from Policy Evaluation section
value.estimate <- mean(Y[A & (W==1)]) * mean(A) + mean(Y[!A & (W==0)]) * mean(!A) value.stderr <- sqrt(var(Y[A & (W==1)]) / sum(A & (W==1)) * mean(A)^2 + var(Y[!A & (W==0)]) / sum(!A & W==0) * mean(!A)^2) print(paste("Value estimate:", value.estimate, "Std. Error:", value.stderr))
As shown, one can estimate the overall expected risk of the outcome in a particular subsample (e.g., test set) by multiplying the conditional probabilities of Y by the estimated prevalences of A under the identified policy.
I have two questions related to this:
(1) What if we wanted to apply this same concept, but in an observational setting? Do you have any code / a modified doubly robust estimator which could help perform this evaluation of the policy tree in the presence of confounding?
(2) Furthermore, what if we wanted to implement this evaluation on the entire sample (rather than only the test sample). Do you have a recommended approach/code that would effectively do this?