A vector containing the outcome of interest. Missing values are not allowed. An outcome vector is not required because the matches generated will be the same regardless of the outcomes. Of course, without any outcomes no causal effect estimates will be produced, only a matched dataset.

A vector indicating the observations which are in the treatment regime and those which are not. This can either be a logical vector or a real vector where 0 denotes control and 1 denotes treatment.

A matrix containing the variables we wish to match on. This matrix may contain the actual observed covariates or the propensity score or a combination of both. All columns of this matrix must have positive variance or Match will return an error.

A matrix containing the covariates for which we wish to make bias adjustments.

A matrix containing the covariates for which the variance of the causal effect may vary. Also see the Var.calc option, which takes precedence.

A character string for the estimand. The default estimand is "ATT", the sample average treatment effect for the treated. "ATE" is the sample average treatment effect, and "ATC" is the sample average treatment effect for the controls.

A scalar for the number of matches which should be found. The default is one-to-one matching. Also see the ties option.

A logical scalar for whether regression adjustment should be used. See the Z matrix.

A logical scalar or vector for whether exact matching should be done. If a logical scalar is provided, that logical value is applied to all covariates in X. If a logical vector is provided, a logical value should be provided for each covariate in X. Using a logical vector allows the user to specify exact matching for some but not other variables. When exact matches are not found, observations are dropped. distance.tolerance determines what is considered to be an exact match. The exact option takes precedence over the caliper option.

A scalar or vector denoting the caliper(s) which should be used when matching. A caliper is the distance which is acceptable for any match. Observations which are outside of the caliper are dropped. If a scalar caliper is provided, this caliper is used for all covariates in X. If a vector of calipers is provided, a caliper value should be provided for each covariate in X. The caliper is interpreted to be in standardized units. For example, caliper=.25 means that all matches not equal to or within .25 standard deviations of each covariate in X are dropped. Note that dropping observations generally changes the quantity being estimated.

A logical flag for whether matching should be done with replacement. Note that if FALSE, the order of matches generally matters. Matches will be found in the same order as the data are sorted. Thus, the match(es) for the first observation will be found first, the match(es) for the second observation will be found second, etc. Matching without replacement will generally increase bias. Ties are randomly broken when replace==FALSE —see the ties option for details.

A logical flag for whether ties should be handled deterministically. By default ties==TRUE. If, for example, one treated observation matches more than one control observation, the matched dataset will include the multiple matched control observations and the matched data will be weighted to reflect the multiple matches. The sum of the weighted observations will still equal the original number of observations. If ties==FALSE, ties will be randomly broken. If the dataset is large and there are many ties, setting ties=FALSE often results in a large speedup. Whether two potential matches are close enough to be considered tied, is controlled by the distance.tolerance option.

This logical flag implements the usual procedure by which observations outside of the common support of a variable (usually the propensity score) across treatment and control groups are discarded. The caliper option is to be preferred to this option because CommonSupport, consistent with the literature, only drops outliers and leaves inliers while the caliper option drops both. If CommonSupport==TRUE, common support will be enforced on the first variable in the X matrix. Note that dropping observations generally changes the quantity being estimated. Use of this option renders it impossible to use the returned objects index.treated and index.control to reconstruct the matched dataset. The returned object mdata will, however, still contain the matched dataset. Seriously, don't use this option; use the caliper option instead.

A scalar for the type of weighting scheme the matching algorithm should use when weighting each of the covariates in X. The default value of 1 denotes that weights are equal to the inverse of the variances. 2 denotes the Mahalanobis distance metric, and 3 denotes that the user will supply a weight matrix (Weight.matrix). Note that if the user supplies a Weight.matrix, Weight will be automatically set to be equal to 3.

This matrix denotes the weights the matching algorithm uses when weighting each of the covariates in X—see the Weight option. This square matrix should have as many columns as the number of columns of the X matrix. This matrix is usually provided by a call to the GenMatch function which finds the optimal weight each variable should be given so as to achieve balance on the covariates.

For most uses, this matrix has zeros in the off-diagonal cells. This matrix can be used to weight some variables more than others. For example, if X contains three variables and we want to match as best as we can on the first, the following would work well:
> Weight.matrix <- diag(3)
> Weight.matrix[1,1] <- 1000/var(X[,1])
> Weight.matrix[2,2] <- 1/var(X[,2])
> Weight.matrix[3,3] <- 1/var(X[,3])
This code changes the weights implied by the inverse of the variances by multiplying the first variable by a 1000 so that it is highly weighted. In order to enforce exact matching see the exact and caliper options.

A vector the same length as Y which provides observation specific weights.

A scalar for the variance estimate that should be used. By default Var.calc=0 which means that homoscedasticity is assumed. For values of Var.calc > 0, robust variances are calculated using Var.calc matches.

A logical flag for whether the population or sample variance is returned.

This is a scalar which is used to determine if distances between two observations are different from zero. Values less than distance.tolerance are deemed to be equal to zero. This option can be used to perform a type of optimal matching

This is a scalar which is used to determine numerical tolerances. This option is used by numerical routines such as those used to determine if a matrix is singular.

A matrix which restricts the possible matches. This matrix has one row for each restriction and three columns. The first two columns contain the two observation numbers which are to be restricted (for example 4 and 20), and the third column is the restriction imposed on the observation-pair. Negative numbers in the third column imply that the two observations cannot be matched under any circumstances, and positive numbers are passed on as the distance between the two observations for the matching algorithm. The most commonly used positive restriction is 0 which implies that the two observations will always be matched.

Exclusion restrictions are even more common. For example, if we want to exclude the observation pair 4 and 20 and the pair 6 and 55 from being matched, the restrict matrix would be: restrict=rbind(c(4,20,-1),c(6,55,-1))

The return object from a previous call to Match. If this object is provided, then Match will use the matches found by the previous invocation of the function. Hence, Match will run faster. This is useful when the treatment does not vary across calls to Match and one wants to use the same set of matches as found before. This often occurs when one is trying to estimate the causal effect of the same treatment (Tr) on different outcomes (Y). When using this option, be careful to use the same arguments as used for the previous invocation of Match unless you know exactly what you are doing.

The version of the code to be used. The "fast" C/C++ version of the code does not calculate Abadie-Imbens standard errors. Additional speed can be obtained by setting ties=FALSE or replace=FALSE if the dataset is large and/or has many ties. The "legacy" version of the code does not make a call to an optimized C/C++ library and is included only for historical compatibility. The "fast" version of the code is significantly faster than the "standard" version for large datasets, and the "legacy" version is much slower than either of the other two.

The estimated average causal effect.

The Abadie-Imbens standard error. This standard error has correct coverage if X consists of either covariates or a known propensity score because it takes into account the uncertainty of the matching procedure. If an estimated propensity score is used, the uncertainty involved in its estimation is not accounted for although the uncertainty of the matching procedure itself still is.

The estimated average causal effect without any BiasAdjust. If BiasAdjust is not requested, this is the same as est.

The usual standard error. This is the standard error calculated on the matched data using the usual method of calculating the difference of means (between treated and control) weighted by the observation weights provided by weights. Note that the standard error provided by se takes into account the uncertainty of the matching procedure while se.standard does not. Neither se nor se.standard take into account the uncertainty of estimating a propensity score. se.standard does not take into account any BiasAdjust. Summary of both types of standard error results can be requested by setting the full=TRUE flag when using the summary.Match function on the object returned by Match.

The conditional standard error. The practitioner should not generally use this.

A list which contains the matched datasets produced by Match. Three datasets are included in this list: Y, Tr and X.

A vector containing the observation numbers from the original dataset for the treated observations in the matched dataset. This index in conjunction with index.control can be used to recover the matched dataset produced by Match. For example, the X matrix used by Match can be recovered by rbind(X[index.treated,],X[index.control,]). The user should generally just examine the output of mdata.

A vector containing the observation numbers from the original data for the control observations in the matched data. This index in conjunction with index.treated can be used to recover the matched dataset produced by Match. For example, the X matrix used by Match can be recovered by rbind(X[index.treated,],X[index.control,]). The user should generally just examine the output of mdata.

A vector containing the observation numbers from the original data which were dropped (if any) in the matched dataset because of various options such as caliper and exact. If no observations were dropped, this index will be NULL.

A vector of weights. There is one weight for each matched-pair in the matched dataset. If all of the observations had a weight of 1 on input, then each matched-pair will have a weight of 1 on output if there are no ties.

The original number of observations in the dataset.

The original number of weighted observations in the dataset.

The original number of treated observations (unweighted).

The number of observations in the matched dataset.

The number of weighted observations in the matched dataset.

The caliper which was used.

The size of the enforced caliper on the scale of the X variables. This object has the same length as the number of covariates in X.

The value of the exact function argument.

The number of weighted observations which were dropped either because of caliper or exact matching. This number, unlike ndrops.matches, takes into account observation specific weights which the user may have provided via the weights argument.

The number of matches which were dropped either because of caliper or exact matching.