Description Usage Arguments References Examples
View source: R/estimateEffects.R
estimateEffects()
is used to estimate the effect of the
treatment along the entire frontier.
1 2 3 4 | estimateEffects(frontier.object, my.form, prop.estimated = 1,
mod.dependence.formula = NULL, continuous.vars = NA,
seed = 1, model.dependence.ests = 100,
means.as.cutpoints = TRUE, alpha=0.95)
|
frontier.object |
An object generated by |
my.form |
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 Athey-Imbens 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. |
model.dependence.ests |
The number of points on the frontier to estimate. Note that for each point, a separate linear model must be fitted, so this can greater increase run time if too large. Defaults to 100 points. |
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. |
alpha |
0.95 by default. Confidence interval for estimated effects. |
King, Gary, Christopher Lucas, and Richard Nielsen. "The Balance-Sample Size Frontier in Matching Methods for Causal Inference." (2015).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | data(lalonde)
match.on <- colnames(lalonde)[!(colnames(lalonde) %in% c('re78',
'treat'))]
my.frontier <- makeFrontier(dataset = lalonde,
treatment = 'treat',
match.on = match.on)
mod.dependence.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 = mod.dependence.form,
continuous.vars = c('age', 'education', 're74', 're75'),
prop.estimated = .1,
means.as.cutpoints = TRUE)
## End(Not run)
|
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