multiMatch: Matching Estimators for Multiple Treatments from Yang et al....

In multilevelMatching: Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments

Description

This function carries out matching on covariates or on propensity scores, and is similar to the 'legacy' functions multilevelMatchX and multilevelGPSMatch.

Usage

multiMatch(Y, W, X, trimming = NULL, match_on,
  model_options = list(reference_level = sort(W)[1]), M_matches = 1,
  J_var_matches = 1)

Arguments

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 σ^2(X,W) as in Abadie and Imbens (2006).

Value

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.

References

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

See Also

multilevelMatchX; multilevelGPSMatch

Examples

  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)

multilevelMatching documentation built on May 8, 2019, 5:02 p.m.