Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTT). The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables) using the methods developed in Firpo (2007) <doi:10.1111/j.1468-0262.2007.00738.x>, (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package e.g. Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> Callaway and Li (2019) <doi:10.3982/QE935>, Callaway, Li, and Oka (2018) <doi:10.1016/j.jeconom.2018.06.008>).

Brantly Callaway

Quantile Treatment Effects

DR function

<dl><dt>yvals</dt>
<dd>A vector of values that y can take</dd>
<dt>drlist</dt>
<dd>A list where for each value of y, a distribution regression</dd></dl>

Arguments

DR — DR

<dl>

<dt>yvals</dt>
<dd>A vector of values that y can take</dd>


<dt>drlist</dt>
<dd>A list where for each value of y, a distribution regression</dd>

</dl>

DR: DR

Description

Usage

Arguments