Propensity score estimation
Description
mnps
calculates propensity scores and diagnoses them using
a variety of methods, but centered on using boosted logistic regression as
implemented in gbm
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
Arguments
formula 
A formula for the propensity score model with the treatment indicator on the left side of the formula and the potential confounding variables on the right side. 
data 
The dataset, includes treatment assignment as well as covariates 
n.trees 
number of gbm iterations passed on to 
interaction.depth 

shrinkage 

bag.fraction 

perm.test.iters 
a nonnegative integer giving the number of iterations
of the permutation test for the KS statistic. If 
print.level 
the amount of detail to print to the screen 
iterlim 
maximum number of iterations for the direct optimization 
verbose 
if TRUE, lots of information will be printed to monitor the the progress of the fitting 
estimand 
The causal effect of interest. Options are 
stop.method 
A method or methods of measuring and summarizing balance across
pretreatment variables. Current options are 
sampw 
Optional sampling weights. 
treatATT 
If the estimand is specified to be ATT, this argument is used to specify which treatment condition is considered 'the treated'. It must be one of the levels of the treatment variable. It is ignored for ATE analyses. 
... 
Additional arguments. 
Details
formula
should be something like "treatment ~ X1 + X2 + X3". The
treatment variable should be a variable with three or more levels. There is no need to specify
interaction terms in the formula. interaction.depth
controls the level
of interactions to allow in the propensity score model.
Note that — unlike earlier versions of twang
— plotting functions
are no longer included in the ps()
function. See
plot
for details of the plots.
Value
Returns an object of class mnps
, which consists of the following.
psList 
A list of 
nFits 
The number of calls to 
estimand 
The estimand – either ATT or ATE – that was specified in the call to 
treatATT 
For ATT fits, the treatment category that is considered "the treated" 
treatLev 
The levels of the treatment variable. 
levExceptTreatAtt 
The levels of the treatment variable, excluding the 
data 
The data used to fit the model. 
treatVar 
The vector of treatment indicators 
stopMethods 
The 
sampw 
Sampling weights provided to 
Author(s)
Lane Burgette burgette@rand.org, Beth Ann Griffin bethg@rand.org, Dan McCaffrey danielm@rand.org
References
Dan McCaffrey, G. Ridgeway, Andrew Morral (2004). “Propensity Score Estimation with Boosted Regression for Evaluating Adolescent Substance Abuse Treatment,” Psychological Methods 9(4):403425.
See Also
ps