Estimate Effects on the Frontier
Description
estimateEffects()
is used to estimate the effect of the
treatment along the entire frontier.
Usage
1 2 3  estimateEffects(frontier.object, formula, prop.estimated = 1,
mod.dependence.formula, continuous.vars = NA,
seed = 1, means.as.cutpoints = FALSE)

Arguments
frontier.object 
An object generated by 
formula 
An object of class formula (or one that can be
coerced to that class). This will be passed to

prop.estimated 
The proportion of points on the frontier to estimate. By default, 100% of the points on the frontier are estimated. To estimate less than 100% of the points, pass the proportion to be estimated to prop.estimated (for example, .6 to estimate 60% of the points). 
mod.dependence.formula 
The formula used as the base formula for the AtheyImbens model dependence estimates. 
continuous.vars 
All continuous control variables in mod.dependence.formula must be passed as a character vector to continuous.vars. A cutpoint for each of these variables will be estimated with segmented regression. 
seed 
The seed used before estimation of the effects. If prop.estimated is less than 1, this is necessary in order to replicate the exact plot. 
means.as.cutpoints 
FALSE by default. If TRUE, cutpoints are calculated as the mean instead of the breakpoint in a segmented regression. This is sometimes much faster. 
References
King, Gary, Christopher Lucas, and Richard Nielsen. "The BalanceSample Size Frontier in Matching Methods for Causal Inference." (2015).
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  data(lalonde)
match.on < colnames(lalonde)[!(colnames(lalonde) %in% c('re78',
'treat'))]
my.frontier < makeFrontier(dataset = lalonde,
treatment = 'treat',
outcome = 're78',
match.on = match.on)
my.form < as.formula(re78 ~ treat + age + black + education + hispanic +
married + nodegree + re74 + re75)
## Not run:
my.estimates < estimateEffects(my.frontier, 're78 ~ treat',
mod.dependence.formula = my.form,
continuous.vars = c('age', 'education', 're74', 're75'),
prop.estimated = .1,
means.as.cutpoints = TRUE)
## End(Not run)
