Propensity score estimation

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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

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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 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):403-425.

See Also

ps

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