mnps: Propensity score estimation

In twang: Toolkit for Weighting and Analysis of Nonequivalent Groups

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

 
mnps(formula = formula(data),
   data,
   n.trees = 10000,
   interaction.depth = 3,
   shrinkage = 0.01,
   bag.fraction = 1.0,
   perm.test.iters=0,
   print.level = 2,
   iterlim = 1000,
   verbose = TRUE, 
   estimand = "ATE",
   stop.method = "es.max", 
   sampw = NULL, 
   treatATT = NULL, ...)

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 gbm
interaction.depth	interaction.depth passed on to gbm
shrinkage	shrinkage passed on to gbm
bag.fraction	bag.fraction passed on to gbm
perm.test.iters	a non-negative integer giving the number of iterations of the permutation test for the KS statistic. If perm.test.iters=0 then the function returns an analytic approximation to the p-value. Setting perm.test.iters=200 will yield precision to within 3% if the true p-value is 0.05. Use perm.test.iters=500 to be within 2%
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 "ATE" (average treatment effect), which attempts to estimate the change in the outcome if the treatment were applied to the entire population versus if the control were applied to the entire population, or "ATT" (average treatment effect on the treated) which attempts to estimate the analogous effect, averaging only over the treated population.
stop.method	A method or methods of measuring and summarizing balance across pretreatment variables. Current options are ks.mean, ks.max, es.mean, and es.max. ks refers to the Kolmogorov-Smirnov statistic and es refers to standardized effect size. These are summarized across the pretreatment variables by either the maximum (.max) or the mean (.mean).
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 ps objects.
nFits	The number of calls to ps that were used to form the mnps object.
estimand	The estimand – either ATT or ATE – that was specified in the call to mnps.
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 treatATT level.
data	The data used to fit the model.
treatVar	The vector of treatment indicators
stopMethods	The stop.method vector specified in the call to mnps.
sampw	Sampling weights provided to mnps, if any.

Author(s)

Lane Burgette [e-mail], Beth Ann Griffin [e-mail], Dan McCaffrey [e-mail]

References

Dan McCaffrey, G. Ridgeway, Andrew Morral (2004). “Propensity Score Estimation with Boosted Regression for Evaluating Adolescent Substance Abuse Treatment,” Psychological Methods 9(4):403-425.

See Also

ps

twang documentation built on May 2, 2019, 6:53 p.m.