cb.align.kway_match {causalBatch}

K-Way matching

Description

A function for performing k-way matching using the matchIt package. Looks for samples which have corresponding matches across all other treatment levels.

Usage

cb.align.kway_match(
  Ts,
  Xs,
  match.form,
  reference = NULL,
  match.args = list(method = "nearest", exact = NULL, replace = FALSE, caliper = 0.1),
  retain.ratio = 0.05
)

Arguments

Ts

[n] the labels of the samples, with K < n levels, as a factor variable.

Xs

[n, r] the r covariates/confounding variables, for each of the n samples, as a data frame with named columns.

match.form

A formula of columns from Xs, to be passed directly to matchit for subsequent matching. See formula argument from matchit for details.

reference

the name of the reference/control batch, against which to match. Defaults to NULL, which treats the reference batch as the smallest batch.

match.args

A named list arguments for the matchit function, to be used to specify specific matching strategies, where the list names are arguments and the corresponding values the value to be passed to matchit. Defaults to inexact nearest-neighbor caliper (width 0.1) matching without replacement.

retain.ratio

If the number of samples retained is less than retain.ratio*n, throws a warning. Defaults to 0.05.

Value

an [m] vector consisting of the sample ids of the n original samples that were retained after matching.

Details

For more details see the help vignette: vignette("causal_balancing", package = "causalBatch")

Author(s)

Eric W. Bridgeford

References

Eric W. Bridgeford, et al. "A Causal Perspective for Batch Effects: When is no answer better than a wrong answer?" Biorxiv (2024).

Daniel E. Ho, et al. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference" JSS (2011).

Examples

library(causalBatch)
sim <- cb.sims.sim_linear(a=-1, n=100, err=1/8, unbalancedness=1.5)
cb.align.kway_match(sim$Ts, data.frame(Covar=sim$Xs), "Covar")