Estimation and Inference for Extreme Continuous Treatment Effects

Bibliographic Data

ID19417954
AuthorsWei Huang (0000-0002-5095-3972, The University of Melbourne), Shuo Li (0000-0002-1633-6444, Tianjin University of Finance and Economics, corresponding author), Liuhua Peng (0000-0002-5431-8079, The University of Melbourne)
Year2025
Volume43
Issue4
Pages822-834
Publication date2025-10-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2024.2430293
OpenAlexW4404525907
LanguageEN
References cited35

This article studies estimation and inference for the treatment effect in deep tails of the potential outcome distributions corresponding to a continuously valued treatment, namely the extreme continuous treatment effect. We consider two measures for the tail characteristics: the quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then, for a quantile level close to 1, we define the extreme quantile treatment effect (EQTE) and extreme average treatment effect (EATE), which are, respectively, the ratios of the quantile and tail mean at different treatment statuses. We propose estimators for the EQTE and EATE based on tail approximations from the extreme value theory. Our limiting theory is for the EQTE and EATE processes indexed by a set of quantile levels and pairs of different treatment statuses. It facilitates uniform inference for the EQTE and EATE over multiple tail levels and multiple treatment effects. Simulations suggest that our method works well in finite samples, and an empirical study illustrates its practical merits

Econometrics · Economics · Estimation · Inference · Statistics · Advanced Causal Inference Techniques · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Statistical Methods and Inference · Artificial Intelligence