36 citations · 56 across the 21 of their papers we have counts for
22 papers
An Instrumental Variable Approach to Account for Informative Treatment Switching in Real-world Evidence
Yang Liu, Andrew Ying, Zongqi Xia +1
Reproducible and generalizable assessment of treatment decisions requires principled handling of subsequent treatment switching that may inform expected outcomes and shift across c…
Efficient Inference for Incremental Causal Effects of Time to Treatment
Zhichen Zhao, Andrew Ying, Ronghui Xu
We consider continuous time to treatment initiation. This can commonly occur in preventive medicine, such as disease screening and vaccination; it can also occur with non-fatal hea…
PAGS: Priority-Adaptive Gaussian Splatting for Dynamic Driving Scenes
Ying A, Wenzhang Sun, Chang Zeng +3
Reconstructing dynamic 3D urban scenes is crucial for autonomous driving, yet current methods face a stark trade-off between fidelity and computational cost. This inefficiency stem…
Dynamic Treatment Effects under Functional Longitudinal Studies
Andrew Ying
Establishing causality is a fundamental goal in fields like medicine and social sciences. While randomized controlled trials are the gold standard for causal inference, they are no…
Deepening the Understanding of Double Robustness Geometrically
Andrew Ying
Double robustness (DR) is a widely-used property of estimators that provides protection against model misspecification and slow convergence of nuisance functions. Despite its wides…
On Defense of the Hazard Ratio
Andrew Ying, Ronghui Xu
In this short communication, we describe the recent debate on whether the hazard function should be used for causal inference in time-to-event studies and consider three different…
Asymptotic Theory for Doubly Robust Estimators with Continuous-Time Nuisance Parameters
Andrew Ying
Doubly robust estimators have gained widespread popularity in various fields due to their ability to provide unbiased estimates under model misspecification. However, the asymptoti…
Proximal Survival Analysis to Handle Dependent Right Censoring
Andrew Ying
Many epidemiological and clinical studies aim at analyzing a time-to-event endpoint. A common complication is right censoring. In some cases, it arises because subjects are still s…
Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation
Yuyao Wang, Andrew Ying, Ronghui Xu
In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event,…
Causal Identification for Complex Functional Longitudinal Studies
Andrew Ying
Real-time monitoring in modern medical research introduces functional longitudinal data, characterized by continuous-time measurements of outcomes, treatments, and confounders. Thi…
Proximal Causal Inference for Marginal Counterfactual Survival Curves
Andrew Ying, Yifan Cui, Eric J. Tchetgen Tchetgen
Contrasting marginal counterfactual survival curves across treatment arms is an effective and popular approach for inferring the causal effect of an intervention on a right-censore…
A Robust Instrumental Variable Method Accounting for Treatment Switching in Open-Label Randomized Controlled Trials
Andrew Ying
In a randomized controlled trial, treatment switching (also called contamination or crossover) occurs when a patient initially assigned to one treatment arm changes to another arm…
Marginal Structural Illness-Death Models for Semi-Competing Risks Data
Yiran Zhang, Andrew Ying, Steve Edland +2
The three state illness death model has been established as a general approach for regression analysis of semi competing risks data. For observational data the marginal structural…
Proximal Causal Inference for Complex Longitudinal Studies
Andrew Ying, Wang Miao, Xu Shi +1
A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata,…
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
AmirEmad Ghassami, Andrew Ying, Ilya Shpitser +1
Robins et al. (2008) introduced a class of influence functions (IFs) which could be used to obtain doubly robust moment functions for the corresponding parameters. However, that cl…
A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model
Andrew Ying, Eric J. Tchetgen Tchetgen
Treatment switching in a randomized controlled trial is said to occur when a patient randomized to one treatment arm switches to another treatment arm during follow-up. This can oc…
An Introduction to Proximal Causal Learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui +2
A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects a…
Causal Effects of Prenatal Drug Exposure on Birth Defects with Missing by Terathanasia
Andrew Ying, Ronghui Xu, Christina D. Chambers +1
A recent cohort study revealed a positive correlate between major structural birth defects in infants and a certain medication taken by pregnant women. To draw valid causal inferen…
On the Asymptotic Distribution of the Scan Statistic for Empirical Distributions
Andrew Ying, Wen-Xin Zhou
We investigate the asymptotic behavior of several variants of the scan statistic applied to empirical distributions, which can be applied to detect the presence of an anomalous int…
A Scan Procedure for Multiple Testing
Shiyun Chen, Andrew Ying, Ery Arias-Castro
In a multiple testing framework, we propose a method that identifies the interval with the highest estimated false discovery rate of P-values and rejects the corresponding null hyp…
Two-Stage Residual Inclusion under the Additive Hazards Model - An Instrumental Variable Approach with Application to SEER-Medicare Linked Data
Andrew Ying, Ronghui Xu, James Murphy
Instrumental variable is an essential tool for addressing unmeasured confounding in observational studies. Two stage predictor substitution (2SPS) estimator and two stage residual…
Detection of Sparse Mixtures: Higher Criticism and Scan Statistic
Ery Arias-Castro, Andrew Ying
We consider the problem of detecting a sparse mixture as studied by Ingster (1997) and Donoho and Jin (2004). We consider a wide array of base distributions. In particular, we stud…