# estimateEffects: Estimate Effects on the Frontier In MatchingFrontier: Computation of the Balance - Sample Size Frontier in Matching Methods for Causal Inference

## 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 `makeFrontier()`. `formula` An object of class formula (or one that can be coerced to that class). This will be passed to `lm()` to estimate the point estimates for the causal effect estimates across the frontier. `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. `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 Balance-Sample 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) ```

MatchingFrontier documentation built on May 29, 2017, 5:35 p.m.