A Convex Programming Solution Based Debiased Estimator for Quantile with Missing Response and High-dimensional Covariables

基于回归秩得分的异质性分位数处理效应的去偏推断

Debiased Inference on Heterogeneous Quantile Treatment Effects with Regression Rank-Scores

本文提出一种结合 $\ell_1$-惩罚回归调整与秩得分偏差校正的新方法，用于高维协变量下异质性分位数处理效应的去偏推断，并通过模拟和英国生物银行数据验证其有效性。

Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond

We consider estimating a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter. A central example is the efficient estimating equation for the (local) quantile treatment effect ((L)QTE) in causal inference, which involves as a nuisance the covariate-conditional cumulative distribution function evaluated at the quantile to be estimated. Debiased machine learning (DML) is a data-splitting approach to estimating high-dimensional nuisances using flexible machine learning methods, but applying it to problems with parameter-dependent nu…

Network and Panel Quantile Effects Via Distribution Regression

This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate,…

高维模型中个体化分位数处理效应的极小极大速率最优推断

Minimax Rate-Optimal Inference for Individualized Quantile Treatment Effects in High-dimensional Models

针对高维模型下的个体化分位数处理效应估计问题，提出去偏估计量，证明其弱收敛于高斯过程，建立极小极大最优推断框架，通过模拟和NHANES数据集验证了方法有效性。

非参数M-估计量的自动去偏机器学习

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands

本文提出自动去偏机器学习框架，用于估计广义统计参数。通过自动构造去偏估计器，结合损失函数的梯度、Hessian和线性近似，实现对非参数M-估计量的光滑函数的高效估计。

高维分位数回归的迁移学习与分布偏移

Transfer Learning for High-dimensional Quantile Regression with Distribution Shift

本文提出了一种针对高维分位数回归的迁移学习方法，以应对参数、协变量和残差偏移问题，通过建立误差界和正交去偏方法提升模型的鲁棒性和准确性。