`method_npcbps.Rd`

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 `CBPS::npCBPS()`

from the CBPS package.

For binary treatments, this method estimates the weights using `CBPS::npCBPS()`

. The ATE is the only estimand allowed. The weights are taken from the output of the `npCBPS`

fit object.

For multinomial treatments, this method estimates the weights using `CBPS::npCBPS()`

. The ATE is the only estimand allowed. The weights are taken from the output of the `npCBPS`

fit object.

For continuous treatments, this method estimates the weights using `CBPS::npCBPS()`

. The weights are taken from the output of the `npCBPS`

fit object.

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.

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 `npCBPS()`

can be passed through `weightit()`

or `weightitMSM()`

.

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

.

`obj`

When

`include.obj = TRUE`

, the nonparametric CB(G)PS model fit. The output of the call to`CBPS::npCBPS()`

.

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.

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

`weightit()`

, `weightitMSM()`

, `method_cbps`

`CBPS::npCBPS()`

for the fitting function

```
# Examples take a long time to run
if (FALSE) {
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)
}
```