make_randomized_compliance_dat

This will generate and randomize a science table to get observed outcomes and treatment assignment

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

make_randomized_compliance_dat function

<dl><dt>n</dt>
<dd>Sample size</dd>
<dt>p</dt>
<dd>Proportion treated</dd>
<dt>science.table.generator</dt>
<dd>Method to generate potential outcomes</dd>
<dt>include.POs</dt>
<dd>Preserve potential outcomes in returned value</dd>
<dt>...</dt>
<dd>To be passed to science.table.generator</dd></dl>

Arguments

Generate fake data with noncompliance. — make_randomized_compliance_dat

<dl>

<dt>n</dt>
<dd>Sample size</dd>


<dt>p</dt>
<dd>Proportion treated</dd>


<dt>science.table.generator</dt>
<dd>Method to generate potential outcomes</dd>


<dt>include.POs</dt>
<dd>Preserve potential outcomes in returned value</dd>


<dt>...</dt>
<dd>To be passed to science.table.generator</dd>

</dl>

make_randomized_compliance_dat: Generate fake data with noncompliance.

Description

Usage

Value

Arguments

See Also