科学研究

彭柳华，美国爱荷华州立大学统计学博士，墨尔本大学数学与统计学院Lecturer in Statistics。研究方向为极值理论、分布式在线学习、高维数据分析、自助法相关理论等。发表论文十余篇，其中多篇发表在Annals of Statistics、Journal of the Royal Statistical Society: Series B、Biometrika、Econometric Theory等统计学与计量经济学国际权威学术期刊上。

This paper concerns estimation and inference for treatment effects in deep tails of the counterfactual distribution of unobservable potential outcomes corresponding to a continuously valued treatment. We consider two measures for the deep tail characteristics: the extreme quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then we define the extreme quantile treatment effect (EQTE) and the extreme average treatment effect (EATE), which can be identified through the commonly adopted unconfoundedness condition and estimated with the aid of extreme value theory. Our limiting theory is for the EQTE and EATE processes indexed by a set of quantile levels and hence facilitates uniform inference. Simulations suggest that our method works well in finite samples and an empirical application illustrates its practical merit.