multilevelMatching implements the estimators introduced in Yang et al. (2016) Propensity Score Matching and Subclassification in Observational studies with Multi-level Treatments: https://doi.org/10.1111/biom.12505. These are covariate- and propensity score-matching estimators for estimating the causal effect of multilevel treatment (i.e., 3 or more treatment types).
The main function for estimation via matching on covariates or propensity
scores is multiMatch
. To carry out estimation via
subclassification, use multilevelGPSStratification
.
1 2 3 4 5 6 7 8 9 10 | sim_data <- multilevelMatching::simulated_data
Y <- sim_data$outcome
W <- sim_data$treatment
X <- as.matrix(sim_data[ ,-(1:2)])
names(Y) <- paste0("ID", 1:length(Y))
trimming <- FALSE
method <- c("covariates", "polr", "multinom")[2]
multiMatch(Y,W,X,trimming=trimming,match_on=method)
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