Bounds the R2 measure (how much of treatment variation is explained by given
covariates) using either the OLS output for the ITT from est.beta, or the
LATE estimation from est.beta.

Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>,
and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <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

R2 function

<dl><dt>est.beta</dt>
<dd>The output from `est.beta()`. Either an estimate of overall
systematic effect variation, or systematic effect variation for compliers.</dd>
<dt>rho.step</dt>
<dd>Grid size for sensitivity analysis on values of rho. Default
is 0.05</dd></dl>

Arguments

Estimate treatment variation R2 — R2

<dl>

<dt>est.beta</dt>
<dd>The output from `est.beta()`. Either an estimate of overall
systematic effect variation, or systematic effect variation for compliers.</dd>


<dt>rho.step</dt>
<dd>Grid size for sensitivity analysis on values of rho. Default
is 0.05</dd>

</dl>

R2: Estimate treatment variation R2

Description

Usage

Value

Arguments

See Also

Examples