GenMatch {Matching}R Documentation

Genetic Matching

Description

This function finds optimal balance using multivariate matching where a genetic search algorithm determines the weight each covariate is given. This function finds the optimal weight each variable should be given by Match so as to achieve balance. Balance is determined by a variety of univariate test, mainly paired t-tests for dichotomous variables and an adjusted univariate Kolmogorov-Smirnov (KS) test for multinomial and continuous variables. The object returned by this function can be supplied to the Weight.matrix option of the Match function to obtain estimates.

Usage

GenMatch(Tr, X, BalanceMatrix=X, estimand="ATT", M=1,
         weights=rep(1,length(Tr)),
         pop.size = 50, max.generations=100,
         wait.generations=4, hard.generation.limit=FALSE,
         starting.values=rep(1,ncol(X)),
         data.type.integer=TRUE,
         MemoryMatrix=TRUE,
         exact=NULL, caliper=NULL, 
         nboots=0, ks=TRUE, verbose=FALSE,
         tolerance = 1e-05,
         distance.tolerance=tolerance,
         min.weight=0, max.weight=1000,
         Domains=NULL, print.level=2,
         project.path=NULL,
         paired=TRUE, ...)

Arguments

Tr 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.
X 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.
BalanceMatrix A matrix containing the variables we wish achieve balance on. This is by default equal to X, but it can in principle be a matrix which contains more or less variables than X or variables which are transformed in various ways. See the examples.
estimand 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 (for all), and "ATC" is the sample average treatment effect for the controls.
M A scalar for the number of matches which should be found (with replacement). The default is one-to-one matching.
weights A vector the same length as Y which provides observations specific weights.
pop.size Population Size. This is the number of individuals genoud uses to solve the optimization problem. See genoud for more details.
max.generations Maximum Generations. This is the maximum number of generations that genoud will run when attempting to optimize a function. This is a soft limit. The maximum generation limit will be binding for genoud only if hard.generation.limit has been set equal to TRUE. If it has not been set equal to TRUE, wait.generations controls when genoud stops. See genoud for more details.
wait.generations If there is no improvement in the objective function in this number of generations, genoud will think that it has found the optimum. The other variables controlling termination are max.generations and hard.generation.limit.
hard.generation.limit This logical variable determines if the max.generations variable is a binding constraint for genoud. If hard.generation.limit is FALSE, then genoud may exceed the max.generations count if the objective function has improved within a given number of generations (determined by wait.generations).
starting.values This vector equal to the number of variables in X. This vector contains the starting weights each of the variables is given. The starting.values vector is a way for the user to insert one individual into the starting population. genoud will randomly create the other individuals. These values correspond to the diagonal of the Weight.matrix as described in detail in the Match function.
data.type.integer By default only integer weights are considered. If this option is set to false, search will be done over floating point weights. This is usually an unnecessary degree of precision.
MemoryMatrix This variable controls if genoud sets up a memory matrix. Such a matrix ensures that genoud will request the fitness evaluation of a given set of parameters only once. The variable may be TRUE or FALSE. If it is FALSE, genoud will be aggressive in conserving memory. The most significant negative implication of this variable being set to FALSE is that genoud will no longer maintain a memory matrix of all evaluated individuals. Therefore, genoud may request evaluations which it has already previously requested. When the number variables in X is large, the memory matrix consumes a large amount of RAM.

genoud's memory matrix will require significantly less memory if the user sets hard.generation.limit equal to TRUE. Doing this is a good way of conserving memory while still making use of the memory matrix structure.
exact 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 of 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. Obviously, if exact matching is done using all of the covariates, one should not be using GenMatch unless the distance.tolerance has been set unusually high.
caliper 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 provide 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. The ecaliper object which is returned by GenMatch shows the enforced caliper on the scale of the X variables.
nboots The number of bootstrap samples to be run for the ks test.
ks A logical flag for if the univariate bootstrap Kolmogorov-Smirnov (KS) test should be calculated. If the ks option is set to true, the univariate KS test is calculated for all non-dichotomous variables. The bootstrap KS test is consistent even for non-continuous variables. See ks.boot for more details.
verbose If details should be printed for each fit evaluation done by the genetic algorithm.
tolerance 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 matrix is singular.
distance.tolerance 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
min.weight This is the minimum weight any variable may be given.
max.weight This is the maximum weight any variable may be given.
Domains This is a ncol(X) *2 matrix. The first column is the lower bound, and the second column is the upper bound for each variable over which genoud will search for weights. If the user does not provide this matrix, the bounds for each variable will be determined by the min.weight and max.weight options.
print.level This option controls the level of printing. There are four possible levels: 0 (minimal printing), 1 (normal), 2 (detailed), and 3 (debug). If level 2 is selected, GenMatch will print details about the population at each generation, including the best individual found so far. If debug level printing is requested, details of the genoud population are printed in the "genoud.pro" file which is located in the temporary R directory returned by the tempdir function. See the project.path option for more details. Because GenMatch runs may take a long time, it is important for the user to receive feedback. Hence, print level 2 has been set as the default.
project.path This is the path of the genoud project file. By default no file is produced unless print.level=3. In that case, genoud places it's output in a file called "genoud.pro" located in the temporary directory provided by tempdir. If a file path is provided to the project.path option, a file will be created regardless of the print.level. The behavior of the project file, however, will depend on the print.level chosen. If the print.level variable is set to 1, then the project file is rewritten after each generation. Therefore, only the currently fully completed generation is included in the file. If the print.level variable is set to 2 or higher, then each new generation is simply appended to the project file. No project file is generated for print.level=0.
paired A flag for if the paired t.test should be used when determining balance.
... Other options which are passed on to genoud.

Details

This function maximizes the smallest p-value that is observed in any of the univariate tests of balance. During optimization, the smallest observed p-value is printed.

Value

value The lowest p-value of the matched dataset.
par A vector of the weights given to each variable in X.
Weight.matrix A matrix whose diagonal corresponds to the weight given to each variable in X. This object corresponds to the Weight.matrix in the Match function.
matches A matrix with three columns. The first column contains the row numbers of the treated observations in the matched dataset. This column corresponds to the index.treated object which is returned by Match. The second column gives the row numbers of the control observations. This column corresponds to the index.control object which is returned by Match. And the last column gives the weight that each matched pair is given. This column corresponds to the weights object which is returned by Match
ecaliper 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.

Author(s)

Jasjeet S. Sekhon, Harvard University, jasjeet_sekhon@harvard.edu, http://jsekhon.fas.harvard.edu/

References

See Also

Also see Match, summary.Match, MatchBalance, genoud, balanceMV, balanceUV, ks.boot, GerberGreenImai, lalonde

Examples

set.seed(38913)

data(lalonde)
attach(lalonde)

#The covariates we want to match on
X = cbind(age, educ, black, hisp, married, nodegr, u74, u75, re75, re74);

#The covariates we want to obtain balance on
BalanceMat <- cbind(age, educ, black, hisp, married, nodegr, u74, u75, re75, re74,
                    I(re74*re75));

#Let's call GenMatch() to find the optimal weight to give each
#covariate in 'X' so as we have achieved balance on the covariates in
#'BalanceMat'. This is only an example so we want GenMatch to be quick
#to the population size has been set to be only 15 via the 'pop.size'
#option.  
genout <- GenMatch(Tr=treat, X=X, BalanceMatrix=BalanceMat, estimand="ATE", M=1,
                   pop.size=16, max.generations=10, wait.generations=1)

#The outcome variable
Y=re78/1000;

# Now that GenMatch() has found the optimal weights, let's estimate
# our causal effect of interest using those weights
mout <- Match(Y=Y, Tr=treat, X=X, estimand="ATE", Weight.matrix=genout)
summary(mout)

#                        
#Let's determine if balance has actually been obtained on the variables of interest
#                        
mb <- MatchBalance(treat~age +educ+black+ hisp+ married+ nodegr+ u74+ u75+
                   re75+ re74+ I(re74*re75),
                   match.out=mout, nboots=500, ks=TRUE, mv=FALSE)


[Package Matching version 0.90 Index]