Estimates propensity scores using the recursive partitioning in a conditional inference framework.

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.

Usage

mlpsa.ctree(vars, formula, level2, ...)

Arguments

vars

a data frame containing the covariates to use for estimating the propensity scores.

formula

the model for estimating the propensity scores. For example, treat ~ .

level2

the name of the column in vars specifying the level 2 (or cluster).

...

currently unused.

Value

a list of BinaryTree-class classes for each level 2

References

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.

See also

[getStrata()]

[tree.plot()]