WeightIt (version 0.7.1)

method_twang: Propensity Score Weighting Using Generalized Boosted Models with TWANG

Description

This page explains the details of estimating weights from generalized boosted model-based propensity scores by setting method = "twang" 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 propensity scores using generalized boosted modeling and then converting those propensity scores into weights using a formula that depends on the desired estimand. The algorithm involves choosing a balance criterion to optimize so that balance, rather than prediction, is prioritized. For binary and multinomial treatments, this method relies on ps from the twang package. For continuous treatments, this method relies on ps.cont from WeightIt.

Binary Treatments

For binary treatments, this method estimates the propensity scores using ps. The following estimands are allowed: ATE, ATT, and ATC. The weights for the ATE, ATT, and ATC are computed from the estimated propensity scores using the standard formulas. When the estimand is the ATE, the return propensity score is the probability of being in the "second" treatment group, i.e., levels(factor(treat))[2]; when the estimand is the ATC, the returned propensity score is the probability of being in the control (i.e., non-focal) group. When include.obj = TRUE, the returned object is the ps fit.

Multinomial Treatments

For multinomial treatments, this method estimates the propensity scores using multiple calls to ps (which is the same thing that mnps in twang does behind the scenes). The following estimands are allowed: ATE and ATT. The weights are computed from the estimated propensity scores using the standard formulas. When include.obj = TRUE, the returned object is a list of the ps fits.

Continuous Treatments

For continuous treatments, the generalized propensity score is estimated using ps.cont. When include.obj = TRUE, the returned object is the ps.cont fit.

Longitudinal Treatments

For longitudinal treatments, the weights are the product of the weights estimated at each time point, which is what iptw in twang does behind the scenes.

Sampling Weights

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

Missing Data

Missing data is compatible with generalized boosted modeling. NA values are simply considered their own plausible value for the covariate. When balance statistics are computed to choose the optimal tree, they are computed using only the non-missing values for the variable in question.

Additional Arguments

Any argument to ps or ps.cont can be passed through weightit or weightitMSM. The argument stop.method is required, and, in contrast to ps and ps.cont, can only be of length 1. If not provided, a default will be used ("es.mean" for binary and multinomial treatments and "p.mean" for continuous treatments).

sampw is ignored because sampling weights are passed using s.weights.

All other arguments take on the defaults of those in ps and ps.cont. The main arguments of interest are n.trees and interaction.depth, and increasing them can improve performance.

References

Binary treatments

McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies. Psychological Methods, 9(4), 403<U+2013>425. 10.1037/1082-989X.9.4.403

Multinomial Treatments

McCaffrey, D. F., Griffin, B. A., Almirall, D., Slaughter, M. E., Ramchand, R., & Burgette, L. F. (2013). A Tutorial on Propensity Score Estimation for Multiple Treatments Using Generalized Boosted Models. Statistics in Medicine, 32(19), 3388<U+2013>3414. 10.1002/sim.5753

Continuous treatments

Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments. Journal of Causal Inference, 3(1).

See Also

weightit, weightitMSM, method_gbm

Examples

# NOT RUN {
# Examples take a while 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 = "twang", estimand = "ATT",
                stop.method = "es.max"))
summary(W1)
bal.tab(W1)

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

#Balancing covariates with respect to re75 (continuous)
(W3 <- weightit(re75 ~ age + educ + married +
                  nodegree + re74, data = lalonde,
                method = "twang", use.kernel = TRUE,
                stop.method = "p.max"))
summary(W3)
bal.tab(W3)
# }