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)

SKS.stat.int.cov(Y, Z, W, X)

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 Aug. 20, 2023, 1:06 a.m.