multiMatch.RdThis function carries out matching on covariates or on propensity scores, and
is similar to the 'legacy' functions multilevelMatchX and
multilevelGPSMatch.
multiMatch(Y, W, X, trimming = NULL, match_on, model_options = list(reference_level = sort(W)[1]), M_matches = 1, J_var_matches = 1)
| Y | A response vector (1 x n) |
|---|---|
| W | A treatment vector (1 x n) with numerical values indicating treatment groups |
| X | A covariate matrix (p x n) with no intercept. When match_on="existing", then X must be a vector (1 x n) of user-specified propensity scores. |
| trimming | an indicator of whether trimming the sample to ensure overlap |
| match_on | User specifies "covariates" to match on raw covariates, or "existing" to match on user-supplied propensity score values, or "polr" or "multinom" to fit a propensity score model. |
| model_options | A list of the options to pass to propensity model. Currently under development. Can only pass reference level to multinomial logistic regression. |
| M_matches | Number of matches per unit for imputing potential outcomes, as in Abadie and Imbens (2006). |
| J_var_matches | Number of matches when estimating \(\sigma^2(X,W)\) as in Abadie and Imbens (2006). |
A list of output from estimateTau, including at most:
tauestimate: a vector of estimates for pairwise treatment
effects
varestimate: a vector of variance estimates for tauestimate,
using Abadie and Imbens (2006)'s method
varestimateAI2016: a vector of variance estimates for
tauestimate, when matching on the generalized propensity score, using
Abadie & Imbens (2016)'s method. This variance estimate takes into account
of the uncertainty in estimating the GPS.
analysis_idx: a list containing the indices_kept (analyzed)
and indices_dropped (trimmed) based on Crump et al. (2009)'s method.
Yang, S., Imbens G. W., Cui, Z., Faries, D. E., & Kadziola, Z. (2016) Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments. Biometrics, 72, 1055-1065. https://doi.org/10.1111/biom.12505
Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. econometrica, 74(1), 235-267. https://doi.org/10.1111/j.1468-0262.2006.00655.x
Abadie, A., & Imbens, G. W. (2016). Matching on the estimated propensity score. Econometrica, 84(2), 781-807. https://doi.org/10.3982/ECTA11293
Crump, R. K., Hotz, V. J., Imbens, G. W., & Mitnik, O. A. (2009). Dealing with limited overlap in estimation of average treatment effects. Biometrika, 96(1), 187-199. https://doi.org/10.1093/biomet/asn055
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)#> -------------- Causal estimates --------------- #> Param Trt1 Trt2 Estimate Variance #> 1 EY(2)-EY(1) 1 2 -0.7303810 0.7261450 #> 2 EY(3)-EY(1) 1 3 0.3711491 0.5966005 #> 3 EY(3)-EY(2) 2 3 1.1015301 0.9743341 #> --- Matching on 'polr' with M=1, J=1 ---