ps: Propensity score estimation

In twang: Toolkit for Weighting and Analysis of Nonequivalent Groups

Description

ps calculates propensity scores and diagnoses them using a variety of methods, but centered on using boosted logistic regression as implemented in gbm

Usage

 
ps(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 = c("ks.mean", "es.mean"), 
   sampw = NULL, 
   multinom = FALSE, ...)

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.
multinom	Set to true only when called from mnps function.
...	Additional arguments.

Details

formula should be something like "treatment ~ X1 + X2 + X3". The treatment variable should be a 0/1 indicator. 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 ps, a list containing

gbm.obj	The returned gbm object
treat	The treatment variable.
desc	a list containing balance tables for each method selected in stop.methods. Includes a component for the unweighted analysis names “unw”. Each desc component includes a list with the following components ess The effective sample size of the control group n.treat The number of subjects in the treatment group n.ctrl The number of subjects in the control group max.es The largest effect size across the covariates mean.es The mean absolute effect size max.ks The largest KS statistic across the covariates mean.ks The average KS statistic across the covariates bal.tab a (potentially large) table summarizing the quality of the weights for equalizing the distribution of features across the two groups. This table is best extracted using the bal.table method. See the help for bal.table for details on the table's contents n.trees The estimated optimal number of gbm iterations to optimize the loss function for the associated stop.methods ps a data frame containing the estimated propensity scores. Each column is associated with one of the methods selected in stop.methods w a data frame containing the propensity score weights. Each column is associated with one of the methods selected in stop.methods. If sampling weights are given then these are incorporated into these weights. estimand The estimand of interest (ATT or ATE).
datestamp	Records the date of the analysis
parameters	Saves the ps call
alerts	Text containing any warnings accumulated during the estimation
iters	A sequence of iterations used in the GBM fits used by plot function.
balance	The balance measures for the pretreatment covariates, with a column for each stop.method.
n.trees	Maximum number of trees considered in GBM fit.
data	Data as specified in the data argument.

Author(s)

Greg Ridgeway [e-mail], Dan McCaffrey [e-mail], Andrew Morral [e-mail], Lane Burgette [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

gbm

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