# absstddif: Absolute standardized differences in means. In designmatch: Matched Samples that are Balanced and Representative by Design

## Description

Function for calculating absolute differences in means between the covariates in the treatment and control groups in terms of the original units of the covariates. Here, the absolute differences in means are normalized by the simple average of the treated and control standard deviations before matching (see Rosenbaum and Rubin 1985 for details).

## Usage

 `1` ``` absstddif(X_mat, t_ind, std_dif) ```

## Arguments

 `X_mat` matrix of covariates: a matrix of covariates used to build the rank-based Mahalanobis distance matrix. `t_ind` treatment indicator: a vector of zeros and ones indicating treatment (1 = treated; 0 = control). `std_dif` standardized differences: a scalar determining the number of absolute standardized differences.

## Value

A vector that can be used with the `mom`, `near` and `far` options of `bmatch` and `nmatch`.

## Author(s)

Jose R. Zubizarreta <[email protected]>, Cinar Kilcioglu <[email protected]>.

## References

Rosenbaum, P. R., and Rubin, D. B. (1985), "Constructing a Control Group by Multivariate Matched Sampling Methods that Incorporate the Propensity Score," The American Statistician, 39, 33-38.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```# Load and attach data data(lalonde) attach(lalonde) # Treatment indicator t_ind = treatment # Constrain differences in means to be at most .05 standard deviations apart mom_covs = cbind(age, education, black, hispanic, married, nodegree, re74, re75) mom_tols = absstddif(mom_covs, t_ind, .05) ```

### Example output

```Loading required package: lattice