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.

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.

Continuous Treatments

For continuous treatments, this method estimates the weights using npCBPS(). The weights are taken from the output of the npCBPS fit object.

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 not supported with method = "npcbps".

Missing Data

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 NAs.

Details

Nonparametric CBPS involves the specification of a constrained optimization problem over the weights. The constraints correspond to covariate balance, and the loss function is the empirical likelihood of the data given the weights. npCBPS is similar to entropy balancing and will generally produce similar results. Because the optimization problem of npCBPS is not convex it can be slow to converge or not converge at all, so approximate balance is allowed instead using the cor.prior argument, which controls the average deviation from zero correlation between the treatment and covariates allowed.

Additional Arguments

All arguments to npCBPS() can be passed through weightit() or weightitMSM().

All arguments take on the defaults of those in npCBPS().

Additional Outputs

obj

When include.obj = TRUE, the nonparametric CB(G)PS model fit. The output of the call to CBPS::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), 156–177. doi: 10.1214/17-AOAS1101

See Also

weightit(), weightitMSM(), method_cbps

CBPS::npCBPS() for the fitting function

Examples