Description Usage Arguments Value References See Also Examples
Matching on GPS with multilevel treatments
1 | multilevelGPSMatch(Y, W, X, Trimming, GPSM = "multinomiallogisticReg")
|
Y |
A continuous 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
|
Trimming |
An indicator of whether trimming the sample to ensure overlap |
GPSM |
An indicator of the methods used for estimating GPS, options
include |
A list element including:
tauestimate
: A vector of estimates for pairwise treatment
effects
varestimate
: A vector of variance estimates for
tauestimate
, using Abadie & Imbens (2006)'s method
varestimateAI2012
: 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. This variable is named AI2012
(not AI2016) for backwards compatibility.
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
multilevelMatchX
;
multilevelGPSStratification
1 2 3 4 5 | X <- c(5.5,10.6,3.1,8.7,5.1,10.2,9.8,4.4,4.9)
Y <- c(102,105,120,130,100,80,94,108,96)
W <- c(1,1,1,3,2,3,2,1,2)
multilevelGPSMatch(Y,W,X,Trimming=0,GPSM="multinomiallogisticReg")
multilevelGPSMatch(Y,W,X,Trimming=1,GPSM="multinomiallogisticReg")
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