WeightIt (version 0.5.1)

method_ebcw: Empirical Balancing Calibration Weighting

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 a function of the weights subject to exact moment balancing constraints. This method relies on ATE from the ATE package.

Binary Treatments

For binary treatments, this method estimates the weights using ATE with ATT = TRUE. The following estimands are allowed: ATE, ATT, and ATC. The weights are taken from the output of the ATE fit object. When the ATE is requested, ATE 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 ATE with ATT = TRUE. The following estimands are allowed: ATE and ATT. The weights are taken from the output of the ATE fit objects. When the ATE is requested, ATE is run once for each treatment group. When the ATT is requested, ATE is run once for each non-focal (i.e., control) group. When include.obj = TRUE, the returned object is the list of ATE 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 ATE can be passed through weightit or weightitMSM, with the following exceptions:

ATT is ignored because the estimand is passed using estimand.

All arguments take on the defaults of those in ATE.

References

Chan, K. C. G., Yam, S. C. P., & Zhang, Z. (2016). Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(3), 673<U+2013>700. 10.1111/rssb.12129

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 = "ebcw", 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 = "ebcw", estimand = "ATE"))
summary(W2)
bal.tab(W2)

# }