WeightIt (version 0.5.1)

method_ebal: Entropy Balancing

Description

This page explains the details of estimating weights using entropy balancing by setting method = "ebal" in the call to weightit or weightitMSM. This method can be used with binary and multinomial treatments.

In general, this method relies on estimating weights by minimizing the entropy of the weights subject to exact moment balancing constraints. This method relies on ebalance from the ebal package.

Binary Treatments

For binary treatments, this method estimates the weights using ebalance. The following estimands are allowed: ATE, ATT, and ATC. The weights are taken from the output of the ebalance fit object. When the ATE is requested, ebalance is run twice, once for each treatment group. When include.obj = TRUE, the returned object is the ebal fit (or a list of the two fits when the estimand is the ATE).

Multinomial Treatments

For multinomial treatments, this method estimates the weights using ebalance. The following estimands are allowed: ATE and ATT. The weights are taken from the output of the ebalance fit objects. When the ATE is requested, ebalance is run once for each treatment group. When the ATT is requested, ebalance is run once for each non-focal (i.e., control) group. When include.obj = TRUE, the returned object is the list of ebalance fits.

Continuous Treatments

Continuous treatments are not supported.

Longitudinal Treatments

For longitudinal treatments, the weights are the product of the weights estimated at each time point. This method is not guaranteed to yield exact balance at each time point.

Sampling Weights

Sampling weights are supported through s.weights in all scenarios.

Additional Arguments

All argument to ebalance can be passed through weightit or weightitMSM, with the following exceptions:

base.weight is ignored because sampling weights are passed using s.weights.

All arguments take on the defaults of those in ebalance.

When standardize = TRUE in the call to weightit, ebalance.trim is run on the resulting ebalance fit objects. Doing so can reduce the variability of the weights while maintaining covariate balance.

References

Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25<U+2013>46. 10.1093/pan/mpr025

See Also

weightit, weightitMSM

Examples

# NOT RUN {
library("cobalt")
data("lalonde", package = "cobalt")

#Balancing covariates between treatment groups (binary)
(W1 <- weightit(treat ~ age + educ + married +
                nodegree + re74, data = lalonde,
                method = "ebal", estimand = "ATT"))
summary(W1)
bal.tab(W1)

#Balancing covariates with respect to race (multinomial)
(W2 <- weightit(race ~ age + educ + married +
                nodegree + re74, data = lalonde,
                method = "ebal", estimand = "ATE",
                standardize = TRUE))
summary(W2)
bal.tab(W2)

# }