2020/05/25 by Liang Jiang, Xiaobin Liu, Jiang, Liang +6 · 1 citation
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Economics and business #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #econ.EM
paper · pdf · doi:10.48550/arxiv.2005.11967
94 pages
openalex publication_date 2020/05/25 · openalex created_date 2020/05/29 · arxiv created 2021/05/13 · arxiv updated 2021/05/14 · openalex updated_date 2026/07/28
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.