
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.
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.
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.
Continuous treatments are not supported.
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. NOTE: the use of entropy balancing with longitudinal treatments has not been validated!
Sampling weights are supported through s.weights
in all scenarios.
In the presence of missing data, the following value(s) for missing
are allowed:
"ind"
(default)First, for each variable with missingness, a new missingness indicator variable is created which takes the value 1 if the original covariate is NA
and 0 otherwise. The missingness indicators are added to the model formula as main effects. The missing values in the covariates are then replaced with 0s (this value is arbitrary and does not affect estimation). The weight estimation then proceeds with this new formula and set of covariates. The covariates output in the resulting weightit
object will be the original covariates with the NA
s.
All arguments to ebalance
can be passed through weightit
or weightitMSM
and take on the defaults of those in ebalance
.
For base.weights
, a vector with length equal to the total number of units can be supplied, in contrast to ebalance()
, which requires a vector with length equal to the number of controls.
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.
obj
When include.obj = TRUE
, the entropy balancing model fit. For binary treatments with estimand = "ATT"
, the output of the call to ebal::ebalance
or ebal::ebalance.trim
when stabilize = TRUE
. For binary treatments with estimand = "ATE"
and multinomial treatments, a list of outputs of calls to ebal::ebalance
or ebal::ebalance.trim
.
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
# 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)
# }
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