1 citations · 1 across the 9 of their papers we have counts for
11 papers
Model-Agnostic Bounds for Augmented Inverse Probability Weighted Estimators' Wald-Confidence Interval Coverage in Randomized Controlled Trials
Hongxiang Qiu
Nonparametric estimators, such as the augmented inverse probability weighted (AIPW) estimator, have become increasingly popular in causal inference. Numerous nonparametric estimato…
Sequentially Doubly Robust Estimation of Conditional Survival Probability with Time-Varying Covariates
Hongxiang Qiu, Marco Carone, Alex Luedtke +1
It is often of interest to study the association between covariates and the cumulative incidence of a right-censored time-to-event outcome. When time-varying covariates are measure…
Training-Set Conditionally Valid Prediction Sets with Right-Censored Data
Wenwen Si, Hongxiang Qiu
Uncertainty quantification of prediction models through prediction sets is increasingly popular and successful, but most existing methods rely on directly observing the outcome and…
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift
Hongxiang Qiu, Eric Tchetgen Tchetgen, Edgar Dobriban
Statistical machine learning methods often face the challenge of limited data available from the population of interest. One remedy is to leverage data from auxiliary source popula…
Doubly Robust Proximal Synthetic Controls
Hongxiang Qiu, Xu Shi, Wang Miao +2
To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treate…
Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: a simulation study
Hongxiang Qiu, Andrea J. Cook, Jennifer F. Bobb
Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce ele…
Prediction Sets Adaptive to Unknown Covariate Shift
Hongxiang Qiu, Edgar Dobriban, Eric Tchetgen Tchetgen
Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructin…
Individualized treatment rules under stochastic treatment cost constraints
Hongxiang Qiu, Marco Carone, Alex Luedtke
Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing…
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior Knowledge
Hongxiang Qiu, Alex Luedtke
Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too…
Universal sieve-based strategies for efficient estimation using machine learning tools
Hongxiang Qiu, Alex Luedtke, Marco Carone
Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One ap…
Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity by Joining Local and Global Information
Amin Javari, HongXiang Qiu, Elham Barzegaran +2
One of the major issues in signed networks is to use network structure to predict the missing sign of an edge. In this paper, we introduce a novel probabilistic approach for the si…