The functions compute a distance matrix, either for a single dataset (i.e.,
the distances between all pairs of units) or for two groups defined by a
splitting variable (i.e., the distances between all units in one group and
all units in the other). These distance matrices include the Mahalanobis
distance, Euclidean distance, scaled Euclidean distance, and robust
(rank-based) Mahalanobis distance. These functions can be used as inputs to
the `distance`

argument to `matchit()`

and are used to compute the
corresponding distance matrices within `matchit()`

when named.

```
mahalanobis_dist(
formula = NULL,
data = NULL,
s.weights = NULL,
var = NULL,
discarded = NULL,
...
)
scaled_euclidean_dist(
formula = NULL,
data = NULL,
s.weights = NULL,
var = NULL,
discarded = NULL,
...
)
robust_mahalanobis_dist(
formula = NULL,
data = NULL,
s.weights = NULL,
discarded = NULL,
...
)
euclidean_dist(formula = NULL, data = NULL, ...)
```

- formula
a formula with the treatment (i.e., splitting variable) on the left side and the covariates used to compute the distance matrix on the right side. If there is no left-hand-side variable, the distances will be computed between all pairs of units. If

`NULL`

, all the variables in`data`

will be used as covariates.- data
a data frame containing the variables named in

`formula`

. If`formula`

is`NULL`

, all variables in`data`

will be used as covariates.- s.weights
when

`var = NULL`

, an optional vector of sampling weights used to compute the variances used in the Mahalanobis, scaled Euclidean, and robust Mahalanobis distances.- var
for

`mahalanobis_dist()`

, a covariance matrix used to scale the covariates. For`scaled_euclidean_dist()`

, either a covariance matrix (from which only the diagonal elements will be used) or a vector of variances used to scale the covariates. If`NULL`

, these values will be calculated using formulas described in Details.- discarded
a

`logical`

vector denoting which units are to be discarded or not. This is used only when`var = NULL`

. The scaling factors will be computed only using the non-discarded units, but the distance matrix will be computed for all units (discarded and non-discarded).- ...
ignored. Included to make cycling through these functions easier without having to change the arguments supplied.

A numeric distance matrix. When `formula`

has a left-hand-side
(treatment) variable, the matrix will have one row for each treated unit and
one column for each control unit. Otherwise, the matrix will have one row
and one column for each unit.

The **Euclidean distance** (computed using `euclidean_dist()`

) is
the raw distance between units, computed as $$d_{ij} = \sqrt{(x_i -
x_j)(x_i - x_j)'}$$ where \(x_i\) and \(x_j\) are vectors of covariates
for units \(i\) and \(j\), respectively. The Euclidean distance is
sensitive to the scales of the variables and their redundancy (i.e.,
correlation). It should probably not be used for matching unless all of the
variables have been previously scaled appropriately or are already on the
same scale. It forms the basis of the other distance measures.

The **scaled Euclidean distance** (computed using
`scaled_euclidean_dist()`

) is the Euclidean distance computed on the
scaled covariates. Typically the covariates are scaled by dividing by their
standard deviations, but any scaling factor can be supplied using the
`var`

argument. This leads to a distance measure computed as
$$d_{ij} = \sqrt{(x_i - x_j)S_d^{-1}(x_i - x_j)'}$$ where \(S_d\) is a
diagonal matrix with the squared scaling factors on the diagonal. Although
this measure is not sensitive to the scales of the variables (because they
are all placed on the same scale), it is still sensitive to redundancy among
the variables. For example, if 5 variables measure approximately the same
construct (i.e., are highly correlated) and 1 variable measures another
construct, the first construct will have 5 times as much influence on the
distance between units as the second construct. The Mahalanobis distance
attempts to address this issue.

The **Mahalanobis distance** (computed using `mahalanobis_dist()`

)
is computed as $$d_{ij} = \sqrt{(x_i - x_j)S^{-1}(x_i - x_j)'}$$ where
\(S\) is a scaling matrix, typically the covariance matrix of the
covariates. It is essentially equivalent to the Euclidean distance computed
on the scaled principal components of the covariates. This is the most
popular distance matrix for matching because it is not sensitive to the
scale of the covariates and accounts for redundancy between them. The
scaling matrix can also be supplied using the `var`

argument.

The Mahalanobis distance can be sensitive to outliers and long-tailed or
otherwise non-normally distributed covariates and may not perform well with
categorical variables due to prioritizing rare categories over common ones.
One solution is the rank-based **robust Mahalanobis distance**
(computed using `robust_mahalanobis_dist()`

), which is computed by
first replacing the covariates with their ranks (using average ranks for
ties) and rescaling each ranked covariate by a constant scaling factor
before computing the usual Mahalanobis distance on the rescaled ranks.

The Mahalanobis distance and its robust variant are computed internally by transforming the covariates in such a way that the Euclidean distance computed on the scaled covariates is equal to the requested distance. For the Mahalanobis distance, this involves replacing the covariates vector \(x_i\) with \(x_iS^{-.5}\), where \(S^{-.5}\) is the Cholesky decomposition of the (generalized) inverse of the covariance matrix \(S\).

When a left-hand-side splitting variable is present in `formula`

and
`var = NULL`

(i.e., so that the scaling matrix is computed internally),
the covariance matrix used is the "pooled" covariance matrix, which
essentially is a weighted average of the covariance matrices computed
separately within each level of the splitting variable to capture
within-group variation and reduce sensitivity to covariate imbalance. This
is also true of the scaling factors used in the scaled Euclidean distance.

Rosenbaum, P. R. (2010). *Design of observational studies*.
Springer.

Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a Control Group Using
Multivariate Matched Sampling Methods That Incorporate the Propensity Score.
*The American Statistician*, 39(1), 33–38. doi:10.2307/2683903

Rubin, D. B. (1980). Bias Reduction Using Mahalanobis-Metric Matching.
*Biometrics*, 36(2), 293–298. doi:10.2307/2529981

`distance`

, `matchit()`

, `dist()`

(which is used
internally to compute Euclidean distances)

`optmatch::match_on()`

, which provides similar functionality but with fewer
options and a focus on efficient storage of the output.

```
data("lalonde")
# Computing the scaled Euclidean distance between all units:
d <- scaled_euclidean_dist(~ age + educ + race + married,
data = lalonde)
# Another interface using the data argument:
dat <- subset(lalonde, select = c(age, educ, race, married))
d <- scaled_euclidean_dist(data = dat)
# Computing the Mahalanobis distance between treated and
# control units:
d <- mahalanobis_dist(treat ~ age + educ + race + married,
data = lalonde)
# Supplying a covariance matrix or vector of variances (note:
# a bit more complicated with factor variables)
dat <- subset(lalonde, select = c(age, educ, married, re74))
vars <- sapply(dat, var)
d <- scaled_euclidean_dist(data = dat, var = vars)
# Same result:
d <- scaled_euclidean_dist(data = dat, var = diag(vars))
# Discard units:
discard <- sample(c(TRUE, FALSE), nrow(lalonde),
replace = TRUE, prob = c(.2, .8))
d <- mahalanobis_dist(treat ~ age + educ + race + married,
data = lalonde, discarded = discard)
dim(d) #all units present in distance matrix
#> [1] 185 429
table(lalonde$treat)
#>
#> 0 1
#> 429 185
```