DOS (version 1.0.0)

smahal: Robust Mahalanobis Distance Matrix for Optimal Matching

Description

Computes a robust Mahalanobis distance matrix between treated individuals and potential controls. The usual Mahalnaobis distance may ignore a variable because it contains one extreme outlier, and it pays too much attention to rare binary variables, but smahal addresses both issues.

Usage

smahal(z, X)

Arguments

z

z is a vector that is 1 for a treated individual and 0 for a control.

X

A matrix of continuous or binary covariates. The number of rows of X must equal the length of z.

Value

The robust distance matrix has one row for each treated individual (z=1) and one column for each potential control (z=0). The row and column names of the distance matrix refer to the position in z, 1, 2, ..., length(z).

Details

The usual Mahalnaobis distance may ignore a variable because it contains one extreme outlier, and it pays too much attention to rare binary variables, but smahal addresses both issues.

To address outliers, each column of x is replaced by a column of ranks, with average ranks used for ties. This prevents one outlier from inflating the variance for a column, thereby making large differences count little in the Mahalanobis distance.

Rare binary variables have very small variances, p(1-p) for small p, so in the usual Mahalanobis distance, a mismatch for a rare binary variable is counted as very important. If you were matching for US states as 49 binary variables, mismatching for California would not be very important, because p(1-p) is not so small, but mismatching for Wyoming is very important because p(1-p) is very small. To combat this, the variances of the ranked columns are rescaled so they are all the same, all equal to the variance of untied ranks. See Chapter 8 of Design of Observational Studies (2010).

References

Rosenbaum, P. R. (2010). Design of Observational Studies. New York: Springer. The method and example are discussed in Chapter 8.

Examples

# NOT RUN {
data(costa)
z<-1*(costa$welder=="Y")
aa<-1*(costa$race=="A")
smoker=1*(costa$smoker=="Y")
age<-costa$age
x<-cbind(age,aa,smoker)
dmat<-smahal(z,x)
# Mahalanobis distances
round(dmat[,1:6],2) # Compare with Table 8.6 in Design of Observational Studies (2010)
# Impose propensity score calipers
prop<-glm(z~age+aa+smoker,family=binomial)$fitted.values # propensity score
# Mahalanobis distanced penalized for violations of a propensity score caliper.
# This version is used for numerical work.
dmat<-addcaliper(dmat,z,prop,caliper=.5)
round(dmat[,1:6],2) # Compare with Table 8.6 in Design of Observational Studies (2010)
# }
# NOT RUN {
# Find the minimum distance match within propensity score calipers.
optmatch::pairmatch(dmat,data=costa)
# }
# NOT RUN {
# Conceptual versions with infinite distances for violations of propensity caliper.
dmat[dmat>20]<-Inf
round(dmat[,1:6],2) # Compare with Table 8.6 in Design of Observational Studies (2010)
# }