Evaluation of continuous treatment

Elena Kotyrlo
Additional contact information
Elena Kotyrlo: HSE University, Moscow, Russian Federation

Applied Econometrics, 2025, vol. 80, 93-116

Abstract: Continuous treatment effect evaluation is complicated by confounders. The dose is commonly related to observed characteristics of the treated unit. This paper provides an overview of approaches for evaluating continuous treatment effects. They are 1) the dose-response function; 2) application to panel data based on difference-in-differences; and 3) conditional treatment effect evaluation with debiased machine learning. Empirical examples of the teleworkability effect on unemployment indicators during the COVID-19 period illustrate the approaches.

Keywords: the generalized propensity score; dose-response function; difference-in-differences; policy evaluation; debiased machine learning; conditional average treatment effect (search for similar items in EconPapers)
JEL-codes: C18 C21 C23 C87 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:apltrx:021848

Access Statistics for this article

Applied Econometrics is currently edited by Anatoly Peresetsky

More articles in Applied Econometrics from Russian Presidential Academy of National Economy and Public Administration (RANEPA)
Bibliographic data for series maintained by Anatoly Peresetsky ().