compute.unc_qte

Computes the QTE or QTT under unconfoundedness. Dispatches to
IPW, OR (outcome regression), or AIPW based on <code>qp$est_method</code> and
<code>qp$target</code>. Called internally by <code>unc_qte</code> and during each
bootstrap iteration.

internal

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 covariates (selection on
observables) using the methods of Firpo (2007)
<doi:10.1111/j.1468-0262.2007.00738.x>, and (iii) identification is based
on a Difference in Differences assumption, with support for several
varieties including Athey and Imbens (2006)
<doi:10.1111/j.1468-0262.2006.00668.x>, Callaway and Li (2019)
<doi:10.3982/QE935>, and Callaway, Li, and Oka (2018)
<doi:10.1016/j.jeconom.2018.06.008>. Version 2.0 adds a unified staggered
treatment adoption API (built on 'ptetools') for all DiD-based estimators,
as well as a new lagged-outcome unconfoundedness estimator ('lou_qtt').

Brantly Callaway

Quantile Treatment Effects

compute.unc_qte function

<dl><dt>qp</dt>
<dd>QTEparams object</dd></dl>

Arguments

compute.unc_qte — compute.unc_qte

<dl>

<dt>qp</dt>
<dd>QTEparams object</dd>

</dl>

compute.unc_qte: compute.unc_qte

Description

Usage

Value

Arguments