WeightIt (version 0.7.1)

method_npcbps: Nonparametric Covariate Balancing Propensity Score Weighting

Description

This page explains the details of estimating weights from nonparametric covariate balancing propensity scores by setting method = "npcbps" 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 maximizing the empirical likelihood of the data subject to balance constraints. This method relies on npCBPS from the CBPS package.

Binary Treatments

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

Multinomial Treatments

For multinomial treatments, this method estimates the weights using npCBPS. The ATE is the only estimand allowed. The weights are taken from the output of the npCBPS fit object. When include.obj = TRUE, the returned object is the npCBPS fit.

Continuous Treatments

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

Longitudinal Treatments

For longitudinal treatments, the weights are the product of the weights estimated at each time point. This is not how CBMSM in the CBPS package estimates weights for longitudinal treatments.

Sampling Weights

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

Missing Data

Missing data is not compatible with the nonparametric CBPS algorithm, so a few extra things happen when NAs are present in the covariates. 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 NAs.

Additional Arguments

All argument to npCBPS can be passed through weightit or weightitMSM.

All arguments take on the defaults of those in npCBPS.

References

Fong, C., Hazlett, C., & Imai, K. (2018). Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements. The Annals of Applied Statistics, 12(1), 156<U+2013>177. 10.1214/17-AOAS1101

See Also

weightit, weightitMSM

Examples

# NOT RUN {
# Examples take a long time to run
# }
# 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 = "npcbps", estimand = "ATE"))
summary(W1)
bal.tab(W1)

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