Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions

DOI10.1080/01621459.2023.2252141MaRDI QIDQ6631718

Jinzhou Li, Sebastian Engelke, Unnamed Author, Marloes H. Maathuis

Publication date: 1 November 2024

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Efficient Semiparametric Estimation of Quantile Treatment Effects
• Causal discovery in heavy-tailed models
• Tail index estimation, concentration and adaptivity
• Estimating catastrophic quantile levels for heavy-tailed distributions
• A simple general approach to inference about the tail of a distribution
• Empirical probability plots and statistical inference for nonlinear models in the two-sample case
• Max-linear models on directed acyclic graphs
• Selecting the optimal sample fraction in univariate extreme value estimation
• Extremal quantile treatment effects
• Extremal quantile regression
• Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks
• The central role of the propensity score in observational studies for causal effects
• The role of the propensity score in estimating dose-response functions
• Estimation of High Conditional Quantiles for Heavy-Tailed Distributions
• Extreme Quantile Estimation Based on the Tail Single-index Model
• Heavy-Tail Phenomena
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• Causal mechanism of extreme river discharges in the upper Danube basin network

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