| Title: | Entropy Reweighting to Create Balanced Samples |
|---|---|
| Description: | Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population. |
| Authors: | Jens Hainmueller |
| Maintainer: | Jens Hainmueller <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1-8 |
| Built: | 2024-08-25 06:27:17 UTC |
| Source: | CRAN |
A function that summarizes the covariate balance statistics that are computed by MatchBalance(Matching) in a balance table.
baltest.collect(matchbal.out, var.names, after = TRUE)baltest.collect(matchbal.out, var.names, after = TRUE)
matchbal.out |
An object from a call to |
var.names |
A vector of covariate names. |
after |
A logical flag for whether the results from before or after Matching should be summarized. If TRUE |
See MatchBalance(Matching) for details.
A matrix that contains the covariate balance statistics in tabular format.
Jens Hainmueller
MatchBalance in the Matching package.
## load(Matching) to run this example ## create toy data: one treatment indicator and three covariates X1-3 #dat <- data.frame(treatment=rbinom(50,size=1,prob=.5),replicate(3,rnorm(50))) #covarsname <- colnames(dat)[-1] ## run balance checks #mout <- MatchBalance(treatment~X1+X2+X3,data=dat) ## summarize in balance table #baltest.collect(matchbal.out=mout,var.names=covarsname,after=FALSE)## load(Matching) to run this example ## create toy data: one treatment indicator and three covariates X1-3 #dat <- data.frame(treatment=rbinom(50,size=1,prob=.5),replicate(3,rnorm(50))) #covarsname <- colnames(dat)[-1] ## run balance checks #mout <- MatchBalance(treatment~X1+X2+X3,data=dat) ## summarize in balance table #baltest.collect(matchbal.out=mout,var.names=covarsname,after=FALSE)
This function is called internally by ebalance and ebalance.trim to implement entropy balancing. This function would normally not be called manually by a user.
eb(tr.total = tr.total, co.x = co.x, coefs = coefs, base.weight = base.weight, max.iterations = max.iterations, constraint.tolerance = constraint.tolerance, print.level = print.level)eb(tr.total = tr.total, co.x = co.x, coefs = coefs, base.weight = base.weight, max.iterations = max.iterations, constraint.tolerance = constraint.tolerance, print.level = print.level)
tr.total |
NA |
co.x |
NA |
coefs |
NA |
base.weight |
NA |
max.iterations |
NA |
constraint.tolerance |
NA |
print.level |
NA |
A list containing the results from the algorithm.
Jens Hainmueller
ebalance, ebalance.trim
##---- NA -----##---- NA -----
This function implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset. The preprocessing is based on a maximum entropy reweighting scheme that assigns weights to each unit such that the covariate distributions in the reweighted data satisfy a set of moment conditions specified by the researcher. This can be useful to balance covariate distributions in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population. The weights that result from entropy balancing can be passed to regression or other models to subsequently analyze the reweighted data.
By default, ebalance reweights the covariate distributions from a control group to match target moments that are computed from a treatment group such that the reweighted data can be used to analyze the average treatment effect on the treated.
ebalance(Treatment, X, base.weight = NULL, norm.constant = NULL, coefs = NULL, max.iterations = 200, constraint.tolerance = 1, print.level = 0)ebalance(Treatment, X, base.weight = NULL, norm.constant = NULL, coefs = NULL, max.iterations = 200, constraint.tolerance = 1, print.level = 0)
Treatment |
A vector indicating the observations which are in the data group that should be reweighted (i.e. the control group) and those which are in the data group that should be used to compute the target moments (i.e. the treatment group). This can either be a logical vector or a real vector where 0 denotes control observations and 1 denotes treatment observations. By default the target moments are computed using the data from all treatment observations. |
X |
A matrix containing the variables that the researchers wants to include in the reweighting. To adjust the means of the covariates, the raw covariates can be included. To adjust the variances of the covariates, squared terms of the raw covariates can be included. To adjust co-moments, interaction terms can be included. All columns of this matrix must have positive variance and the matrix must be invertible. No missing data is allowed. |
base.weight |
An optional vector of base weights for the maximum entropy reweighting (one weight for each control unit). The default is uniform base weights. |
norm.constant |
An optional normalizing constant. By default the weights are normalized such that the sum of the weights for the reweighted control group match the number of observations in the treatment grou. |
coefs |
An optional vector of model coefficients to start the reweighting. |
max.iterations |
Maximum number of iterations that will be run when attempting to reweight the data. |
constraint.tolerance |
This is the tolerance level used by |
print.level |
Controls the level of printing: 0 (normal printing), 2 (detailed), and 3 (very detailed). |
If the user wants to pass different target moments these should be entered in a new row in the dataset which contains the target means for the covariate distributions.
An list object of class ebalance with the following elements:
target.margins |
A vector that contains the target moments coded from the covariate distributions of the treatment group. |
co.xdata |
A matrix that contains the covariate data from the control group. |
w |
A vector that contains the control group weights assigned by entropy balancing. |
coefs |
A vector that contains coefficients from the reweighting algorithm. |
maxdiff |
A scalar that contains the maximum deviation between the moments of the reweighted data and the target moments. |
constraint.tolerance |
The tolerance level used for the balance constraints. |
base.weight |
The base weight used. |
print.level |
The print level used. |
converged |
Logical flag if algorithm converged within tolerance. |
Jens Hainmueller
Hainmueller, J. (2012) 'Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies', Political Analysis (Winter 2012) 20 (1): 25–46.
Zaslavsky, A. (1988), 'Representing local reweighting area adjustments by of households', Survey Methodology 14(2), 265–288.
Ireland, C. and Kullback, S. (1968), 'Contingency tables with given marginals', Biometrika 55, 179–188.
Kullback, S. (1959), Information Theory and Statistics, Wiley, NY.
Also see ebalance.trim.
# create toy data: treatment indicator and three covariates X1-3 treatment <- c(rep(0,50),rep(1,30)) X <- rbind(replicate(3,rnorm(50,0)),replicate(3,rnorm(30,.5))) colnames(X) <- paste("x",1:3,sep="") # entropy balancing eb.out <- ebalance(Treatment=treatment, X=X) # means in treatment group data apply(X[treatment==1,],2,mean) # means in reweighted control group data apply(X[treatment==0,],2,weighted.mean,w=eb.out$w) # means in raw data control group data apply(X[treatment==0,],2,mean)# create toy data: treatment indicator and three covariates X1-3 treatment <- c(rep(0,50),rep(1,30)) X <- rbind(replicate(3,rnorm(50,0)),replicate(3,rnorm(30,.5))) colnames(X) <- paste("x",1:3,sep="") # entropy balancing eb.out <- ebalance(Treatment=treatment, X=X) # means in treatment group data apply(X[treatment==1,],2,mean) # means in reweighted control group data apply(X[treatment==0,],2,weighted.mean,w=eb.out$w) # means in raw data control group data apply(X[treatment==0,],2,mean)
Function to trim weights obtained from entropy balancing. It takes the output from a call to ebalance and trims the data weights (subject to the moment conditions) such that the
ratio of the maximum/minimum weight to the mean weight is
reduced to satisfy a user specified target. If no user target is specified the maximum weight ratio is automatically trimmed as far as is feasible given the data.
ebalance.trim(ebalanceobj, max.weight = NULL, min.weight = 0, max.trim.iterations = 200, max.weight.increment = 0.92, min.weight.increment = 1.08, print.level = 0)ebalance.trim(ebalanceobj, max.weight = NULL, min.weight = 0, max.trim.iterations = 200, max.weight.increment = 0.92, min.weight.increment = 1.08, print.level = 0)
ebalanceobj |
An object from a call to |
max.weight |
Optional target for the ratio of the maximum to mean weight. |
min.weight |
Optional target for the ratio of the minimum to mean weight. |
max.trim.iterations |
Maximum number of trimming iterations. |
max.weight.increment |
Increment for iterative trimming of the ratio of the maximum to mean weight (a scalar between 0-1, .92 indicates that the attempted reduction in the max ratio is 8 percent). |
min.weight.increment |
Increment for iterative trimming of the ratio of the minimum to mean weight (a scalar > 1, 1.08 indicates that the attempted reduction in the max ratio is 8 percent). |
print.level |
Controls the level of printing: 0 (normal printing), 2 (detailed), and 3 (very detailed). |
An list object of class ebalance.trim with the following elements:
target.margins |
A vector that contains the target moments coded from the covariate distributions of the treatment group. |
co.xdata |
A matrix that contains the covariate data from the control group. |
w |
A vector that contains the control group weights assigned by trimming entropy balancing algorithm. |
coefs |
A vector that contains coefficients from the reweighting algorithm. |
maxdiff |
A scalar that contains the maximum deviation between the moments of the reweighted data and the target moments. |
norm.constant |
Normalizing constant used. |
constraint.tolerance |
The tolerance level used for the balance constraints. |
max.iterations |
Maximum number of trimming iterations used. |
base.weight |
The base weight used. |
converged |
Logical flag if algorithm converged within tolerance. |
Jens Hainmueller
Hainmueller, J. (2012) 'Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies', Political Analysis (Winter 2012) 20 (1): 25–46.
Zaslavsky, A. (1988), 'Representing local reweighting area adjustments by of households', Survey Methodology 14(2), 265–288.
Ireland, C. and Kullback, S. (1968), 'Contingency tables with given marginals', Biometrika 55, 179–188.
Kullback, S. (1959), Information Theory and Statistics, Wiley, NY.
Also see ebalance.trim.
# create toy data: treatment indicator and three covariates X1-3 treatment <- c(rep(0,50),rep(1,30)) X <- rbind(replicate(3,rnorm(50,0)),replicate(3,rnorm(30,.5))) colnames(X) <- paste("x",1:3,sep="") # entropy balancing eb.out <- ebalance(Treatment=treatment, X=X) # means in treatment group data apply(X[treatment==1,],2,mean) # means in reweighted control group data apply(X[treatment==0,],2,weighted.mean,w=eb.out$w) # means in raw data control group data apply(X[treatment==0,],2,mean) # trim weights eb.out.tr <- ebalance.trim(eb.out) # means in reweighted control group data apply(X[treatment==0,],2,weighted.mean,w=eb.out.tr$w) # untrimmed and trimmed weights round(summary(eb.out$w),2) round(summary(eb.out.tr$w),2)# create toy data: treatment indicator and three covariates X1-3 treatment <- c(rep(0,50),rep(1,30)) X <- rbind(replicate(3,rnorm(50,0)),replicate(3,rnorm(30,.5))) colnames(X) <- paste("x",1:3,sep="") # entropy balancing eb.out <- ebalance(Treatment=treatment, X=X) # means in treatment group data apply(X[treatment==1,],2,mean) # means in reweighted control group data apply(X[treatment==0,],2,weighted.mean,w=eb.out$w) # means in raw data control group data apply(X[treatment==0,],2,mean) # trim weights eb.out.tr <- ebalance.trim(eb.out) # means in reweighted control group data apply(X[treatment==0,],2,weighted.mean,w=eb.out.tr$w) # untrimmed and trimmed weights round(summary(eb.out$w),2) round(summary(eb.out.tr$w),2)
Takes a matrix of covariates and generates a new matrix that contains the original covariates and all squared terms. Squared terms for binary covariates are omitted.
getsquares(mat)getsquares(mat)
mat |
n by k numeric matrix of covariates. |
n by k*2 numeric matrix that contains the original covariates plus all squared terms.
Jens Hainmueller
See matrixmaker
# create toy matrix mold <- replicate(3,rnorm(50)) colnames(mold) <- paste("x",1:3,sep="") head(mold) # create new matrix mnew <- getsquares(mold) head(mnew)# create toy matrix mold <- replicate(3,rnorm(50)) colnames(mold) <- paste("x",1:3,sep="") head(mold) # create new matrix mnew <- getsquares(mold) head(mnew)
Function called internally by ebalance and ebalance.trim to compute optimal step length for entropy balancing algorithm. This function would normally not be called manually by a user.
line.searcher(Base.weight, Co.x, Tr.total, coefs, Newton, ss)line.searcher(Base.weight, Co.x, Tr.total, coefs, Newton, ss)
Base.weight |
NA |
Co.x |
NA |
Tr.total |
NA |
coefs |
NA |
Newton |
NA |
ss |
NA |
A list with the results from the search.
Jens Hainmueller
ebalance, ebalance.trim
##---- NA -----##---- NA -----
Takes a matrix of covariates and generates a new matrix that contains the original covariates, all one-way interaction terms, and all squared terms.
matrixmaker(mat)matrixmaker(mat)
mat |
n by k numeric matrix of covariates. |
n by (k*(k+1))/2 +1) matrix of covariates with original covariates, all one-way interaction terms, and all squared terms.
Jens Hainmueller
See getsquares
# create toy matrix mold <- replicate(3,rnorm(50)) colnames(mold) <- paste("x",1:3,sep="") head(mold) # create new matrix mnew <- matrixmaker(mold) head(mnew)# create toy matrix mold <- replicate(3,rnorm(50)) colnames(mold) <- paste("x",1:3,sep="") head(mold) # create new matrix mnew <- matrixmaker(mold) head(mnew)