cb.align.vm_trim {causalBatch}

Vector Matching

Description

A function for implementing the vector matching procedure, a pre-processing step for causal conditional distance correlation. Uses propensity scores to strategically include/exclude samples from subsequent inference, based on whether (or not) there are samples with similar propensity scores across all treatment levels (conceptually, a k-way "propensity trimming"). It is imperative that this function is used in conjunction with domain expertise to ensure that the covariates are not colliders, and that the system satisfies the strong ignorability condiiton to derive causal conclusions.

Usage

cb.align.vm_trim(Ts, Xs, retain.ratio = 0.05, ddx = FALSE)

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.

retain.ratio

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

ddx

whether to show additional diagnosis messages. Defaults to FALSE. Can help with debugging if unexpected results are obtained.

Value

a [m] vector containing the indices of samples retained after vector matching.

Details

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

Author(s)

Eric W. Bridgeford

References

Michael J. Lopez, et al. "Estimation of Causal Effects with Multiple Treatments" Statistical Science (2017). ran

Examples

library(causalBatch)
sim <- cb.sims.sim_linear(a=-1, n=100, err=1/8, unbalancedness=3)
cb.align.vm_trim(sim$Ts, sim$Xs)