mlr: Creating a series of matched data sets with different...

In MatchLinReg: Combining Matching and Linear Regression for Causal Inference

Creating a series of matched data sets with different calibration parameters

Description

Creating a series of matched data sets with different calibration parameters. The output of this function can be supplied to summary.mlr and then plot.summary.mlr methods to generate diagnostic and calibration plots.

Usage

mlr(tr, Z.i = NULL, Z.o = mlr.generate.Z.o(Z.i), psm = TRUE
  , caliper.vec = c(0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 5, Inf)
  , ...)

Arguments

tr	Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.
Z.i	Matrix of adjustment covariates included in linear regression. We must have nrow(Z.i) == length(tr).
Z.o	Matrix of adjustment covariates (present in generative model but) omitted from regression estimation. We must have nrow(Z.o) == length(tr).
psm	Boolean flag, indicating whether propensity score matching should be used (TRUE) or Mahalanobis matching (FALSE).
caliper.vec	Vector of matching calipers used.
...	Other parameters passed to mlr.match.

Value

A list with the following fields:

tr	Same as input.
Z.i	Same as input.
Z.o	Same as input.
idx.list	List of observation indexes for each matched data set.
caliper.vec	Same as input.

Author(s)

Alireza S. Mahani, Mansour T.A. Sharabiani

References

Link to a draft paper, documenting the supporting mathematical framework, will be provided in the next release.

MatchLinReg documentation built on Aug. 30, 2022, 5:05 p.m.