In jbryer/psa: Applied Propensity Score Analysis with R

Methods for Estimating Propensity Scores {#appendix-psmodels}

This appendix provide R code for multiple statistical models for estimating propensity scores. The examples use the lalonde dataset with the following formula:

lalonde.formu <- treat ~ age + I(age^2) + educ + I(educ^2) + black +
    hisp + married + nodegr + re74  + I(re74^2) + re75 + I(re75^2) +
    u74 + u75

Logistic Regression

lr_out <- glm(lalonde.formu,
              data = lalonde,
              family = binomial(link = logit))
lr_ps <- fitted(lr_out)

Conditional Inference Trees with `party` package

library(party)
ctree_out <- ctree(lalonde.formu,
                   data = lalonde)
# treeresponse(ctree_out)

Recusrive Partitioning with `rpart`

library(rpart)
rpart_out <- rpart(lalonde.formu,
                   data = lalonde,
                   method = 'class')
# For classification
rpart_strata <- rpart_out$where
# For matching or weighting
rpart_ps <- predict(rpart_out, type = 'prob')[,1]

Bayesian Logistic Regression

library(rstanarm)
stan_out <- stan_glm(lalonde.formu,
                     data = lalonde)
stan_ps <- predict(stan_out, type = 'response')

Probit BART for dichotomous outcomes with Normal latents

library(BART)
bart_out <- pbart(x.train = lalonde[,all.vars(lalonde.formu)[-1]],
                  y.train = lalonde[,all.vars(lalonde.formu)[1]])
bart_ps <- bart_out$prob.test.mean

Random Forests

library(randomForest)
rf_out <- randomForest(update.formula(lalonde.formu, factor(treat) ~ .),
                       data = lalonde)
# For classification
rf_strata <- predict(rf_out, type = 'response')
# For matching or weighting
rf_ps <- predict(rf_out, type = 'prob')[,1,drop=TRUE]