ebalance.trim: 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.

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.

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.

Examples

Run this code

# NOT RUN {
# 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)

# }