SKS_stat_int_cov: SKS_stat_int_cov_pool

In hettx: Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation

SKS_stat_int_cov_pool

Description

SKS_stat_int_cov_pool is a shifted kolmogorov-smirnov statistic with a linear treatment effect model defined by W. It will attempt to remove any systematic variation corresponding to W and then return a SKS statistic on the residuals to measure any variation "left over".

SKS_stat_int_cov() is a Shifted kolmogorov-smirnov statistic with a linear treatment effect model defined by W. It will attempt to remove any systematic variation corresponding to W and then return a SKS statistic on the residuals to measure any variation "left over".

Usage

SKS_stat_int_cov_pool(Y, Z, W, X = NULL)

SKS_stat_int_cov(Y, Z, W, X = NULL)

Arguments

Y	Observed outcome vector
Z	Treatment assigment vector
W	Additional pre-treatment covariates to interact with T to define linear model of treatment effects.
X	Additional pre-treatment covariates to adjust for in estimation, but not to interact with treatment.

Details

X are _additional_ covariates to adjust for beyond those involved in treatment effect model. It will automatically ajust for W as well. Do not put a covariate in for both X and W.

This is the test statistic used in Ding, Feller, and Miratrix (2016), JRSS-B.

SKS_stat_int_cov first adjusts for baseline and then models treatment effect on the residuals to not split treatment effects (see the vignette for more information on this).

We recommend SKS_stat_int_cov over the "pool" method.

Examples

df <- make_randomized_dat( 1000, gamma.vec=c(1,1,1,2), beta.vec=c(-1,-1,1,0) )
SKS_stat_int_cov_pool(Y = df$Yobs, Z = df$Z, W = df$A, X = df$B)


df <- make_randomized_dat( 1000, gamma.vec=c(1,1,1,2), beta.vec=c(-1,-1,1,0) )
SKS_stat_int_cov(Y = df$Yobs, Z = df$Z, W = df$A, X = df$B)

hettx documentation built on Feb. 24, 2026, 5:08 p.m.