SKS_pool_t

Subtract off group level treatment effect estimates and then look
at KS statistic on residuals.

Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <doi:10.48550/arXiv.1412.5000>,
and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <doi:10.48550/arXiv.1605.06566>
for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

Ben Fifield

hettx

Fisherian and Neymanian Methods for Detecting and Measuring
Treatment Effect Variation

Peng Ding

Avi Feller

Luke Miratrix

SKS_pool_t function

<dl><dt>Y</dt>
<dd>Observed outcome vector</dd>
<dt>Z</dt>
<dd>Treatment assigment vector</dd>
<dt>W</dt>
<dd>A a factor or categorical covariate.</dd></dl>

Arguments

SKS_pool_t — SKS_pool_t

<dl>

<dt>Y</dt>
<dd>Observed outcome vector</dd>


<dt>Z</dt>
<dd>Treatment assigment vector</dd>


<dt>W</dt>
<dd>A a factor or categorical covariate.</dd>

</dl>

SKS_pool_t: SKS_pool_t

Description

Usage

Arguments

Details

Examples