get_testing_grid

Estimates a linear model <code>Y ~ Z + W + Z:W (+X)</code> and samples from
the confidence region of the Z-related coefficients. For each sampled
model, computes individual imputed treatment effects and science tables
of imputed potential outcomes.

internal

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

get_testing_grid function

<dl><dt>Y</dt>
<dd>Outcome vector.</dd>
<dt>Z</dt>
<dd>Treatment assignment vector (0/1).</dd>
<dt>W</dt>
<dd>Covariate matrix for treatment effect interactions.</dd>
<dt>X</dt>
<dd>Optional covariate matrix for additional adjustment.</dd>
<dt>gamma</dt>
<dd>Tail probability for the confidence region. Default is 0.0001.</dd>
<dt>grid.size</dt>
<dd>Number of models to sample. Default is 150.</dd></dl>

Arguments

Build a grid of treatment effect models for interaction testing — get_testing_grid

<dl>

<dt>Y</dt>
<dd>Outcome vector.</dd>


<dt>Z</dt>
<dd>Treatment assignment vector (0/1).</dd>


<dt>W</dt>
<dd>Covariate matrix for treatment effect interactions.</dd>


<dt>X</dt>
<dd>Optional covariate matrix for additional adjustment.</dd>


<dt>gamma</dt>
<dd>Tail probability for the confidence region. Default is 0.0001.</dd>


<dt>grid.size</dt>
<dd>Number of models to sample. Default is 150.</dd>

</dl>

get_testing_grid: Build a grid of treatment effect models for interaction testing

Description

Usage

Value

Arguments