I am trying to estimate propensity scores in R. By this I mean that I am trying to estimate probability that an individual selects into treatment, where the selection into treatment is a binary variable. Typically people use logit or probit to estimate these. I was thinking of using regression trees.
Is it okay to use rpart "anova" method to estimate propensities? I think I cannot use classification tree because that gives a binary outcome whereas I want the continuous propensity score function i.e. how does probability of treatment changes as the continuous explanatory variables changes.
I am using the following command where x and z are continuous explanatory variables and d is a binary treatment variable and all 3 are observed for each individual and I want to estimate P(D=1|X,Z).
cart = rpart(d~x+z, data=data, method="anova") data[,pz_c:=predict(cart, type = "vector")]
where the first command estimates the propensity and the second adds the predicted propensities to the data set for each individual.
I originally posted this on stackoverflow and was told to post thee question here instead.