WeightIt (version 0.5.1)

method_optweight: Optimization-Based Weighting

Description

This page explains the details of estimating optimization-based weights by setting method = "optweight" in the call to weightit or weightitMSM. This method can be used with binary, multinomial, and continuous treatments.

In general, this method relies on estimating weights by solving a quadratic programming problem subject to approximate or exact balance constraints. This method relies on optweight from the optweight package.

Because optweight offers finer control and uses the same syntax as weightit, it is recommended that optweight be used instead of weightit with method = "optweight".

Binary Treatments

For binary treatments, this method estimates the weights using optweight. The following estimands are allowed: ATE, ATT, and ATC. The weights are taken from the output of the optweight fit object. When include.obj = TRUE, the returned object is the optweight fit.

Multinomial Treatments

For multinomial treatments, this method estimates the weights using optweight. The following estimands are allowed: ATE and ATT. The weights are taken from the output of the optweight fit object. When include.obj = TRUE, the returned object is the optweight fit.

Continuous Treatments

For binary treatments, this method estimates the weights using optweight. The weights are taken from the output of the optweight fit object. When include.obj = TRUE, the returned object is the optweight fit.

Longitudinal Treatments

For longitudinal treatments, optweight estimates weights that simultaneously satisfy balance constraints at all time points, so only one model is fit to obtain the weights. Using method = "optweight" in weightitMSM cause is.MSM.method to be set to TRUE by default. Setting it to FALSE will run one model for each time point and multiply the weights together, a method that is not recommended.

Sampling Weights

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

Additional Arguments

All arguments to optweight can be passed through weightit or weightitMSM, with the following exception:

targets cannot be used and is ignored.

All arguments take on the defaults of those in optweight.

References

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910<U+2013>922. 10.1080/01621459.2015.1023805

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 = "optweight", estimand = "ATT",
                tols = 0))
summary(W1)
bal.tab(W1)

#Balancing covariates with respect to race (multinomial)
(W2 <- weightit(race ~ age + educ + married +
                nodegree + re74, data = lalonde,
                method = "optweight", estimand = "ATE",
                tols = .01))
summary(W2)
bal.tab(W2)

#Balancing covariates with respect to re75 (continuous)
(W3 <- weightit(re75 ~ age + educ + married +
                nodegree + re74, data = lalonde,
                method = "optweight", tols = .05))
summary(W3)
bal.tab(W3)
# }