ebalance.trim: Trimming of Weights for Entropy Balancing

In ebal: Entropy Reweighting to Create Balanced Samples

Trimming of Weights for Entropy Balancing

Description

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.

Usage

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)

Arguments

ebalanceobj	An object from a call to ebalance.
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).

Value

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.

Author(s)

Jens Hainmueller

References

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.

See Also

Also see ebalance.trim.

Examples

# 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)

ebal documentation built on June 9, 2022, 9:05 a.m.