ps: Propensity score estimation

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.i

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 populat

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 sta

sampw

Optional sampling weights.

multinom

Set to true only when called from mnps function.

...

Additional arguments.

Value

Returns an object of class ps, a list containing
gbm.objThe returned gbm object
treatThe treatment variable.
desca 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 [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
datestampRecords the date of the analysis
parametersSaves the ps call
alertsText containing any warnings accumulated during the estimation
itersA sequence of iterations used in the GBM fits used by plot function.
balanceThe balance measures for the pretreatment covariates, with a column for each stop.method.
n.treesMaximum number of trees considered in GBM fit.
dataData as specified in the data argument.

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.

References