This function will estimate propensity scores using the conditional inference
framework as outlined in the party package. Specifically, a separate
tree will be estimated for each level 2 (or cluster). A key advantage of this
framework over other methods for estimating propensity scores is that this
method will work on data sets containing missing values.
mlpsa.ctree(vars, formula, level2, ...)a data frame containing the covariates to use for estimating the propensity scores.
the model for estimating the propensity scores. For example, treat ~ .
the name of the column in vars specifying the level 2 (or cluster).
currently unused.
a list of BinaryTree-class classes for each level 2
Torsten Hothorn, Kurt Hornik and Achim Zeileis (2006). Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651--674.