483 citations · 1.3k across the 30 of their papers we have counts for
33 papers
Apportioning Causal Responsibility of Two Risk Factors for an Adverse Outcome via Counterfactual Attribution
Shanshan Luo, Yafang Deng, Qingyuan Zhao +1
Unlike traditional causal inference, which prospectively evaluates the effects of causes, apportioning causal responsibility requires a retrospective assessment to deduce the cause…
Heritability: A Counterfactual Perspective
Haochen Lei, Jieru Shi, Hongyuan Cao +1
Heritability is a central concept in the long-standing debate about nature versus nurture in biological and social sciences. However, existing notions of heritability are based on…
Simultaneous false discovery rate control in location families
Zijun Gao, Wenjie Hu, Qingyuan Zhao
When testing a number of statistical hypotheses using data from location families, it is often useful to control the false discovery rate (FDR) not just for hypotheses of the null…
Simultaneous Hypothesis Testing Using Internal Negative Controls with An Application to Proteomics
Zijun Gao, Qingyuan Zhao
Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (''negative result'') is expected. Motivated by a real p…
Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations
Tobias Freidling, Qingyuan Zhao
Causal inference necessarily relies upon untestable assumptions; hence, it is crucial to assess the robustness of obtained results to violations of identification assumptions. Howe…
An average-case sensitivity analysis for unmeasured confounding
Yao Zhang, Qingyuan Zhao
Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal sensitivity model gained popularity recently due to goo…
A unified analysis of regression adjustment in randomized experiments
Katarzyna Reluga, Ting Ye, Qingyuan Zhao
Regression adjustment is broadly applied in randomized trials under the premise that it usually improves the precision of a treatment effect estimator. However, previous work has s…
Almost exact Mendelian randomization
Matthew J Tudball, George Davey Smith, Qingyuan Zhao
Mendelian randomization (MR) is a natural experimental design based on the random transmission of genes from parents to offspring. However, this inferential basis is typically only…
Confounder Selection: Objectives and Approaches
F. Richard Guo, Anton Rask Lundborg, Qingyuan Zhao
Confounder selection is perhaps the most important step in the design of observational studies. A number of criteria, often with different objectives and approaches, have been prop…
What is a randomization test?
Yao Zhang, Qingyuan Zhao
The meaning of randomization tests has become obscure in statistics education and practice over the last century. This article makes a fresh attempt at rectifying this core concept…
Multiple conditional randomization tests for lagged and spillover treatment effects
Yao Zhang, Qingyuan Zhao
We consider the problem of constructing multiple independent conditional randomization tests using a single dataset. Because the tests are independent, the randomization p-values c…
On testing mean proportionality of multivariate normal variables
Etaash Katiyar, Qingyuan Zhao
This short note considers the problem of testing the null hypothesis that the mean values of two multivariate normal variables are proportional. We show that the usual likelihood r…
Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
Ting Ye, Jun Shao, Yanyao Yi +1
In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model…
A note on post-treatment selection in studying racial discrimination in policing
Qingyuan Zhao, Luke J Keele, Dylan S Small +1
We discuss some causal estimands used to study racial discrimination in policing. A central challenge is that not all police-civilian encounters are recorded in administrative data…
A Latent Mixture Model for Heterogeneous Causal Mechanisms in Mendelian Randomization
Daniel Iong, Qingyuan Zhao, Yang Chen
Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for causal inference. Existing MR methods usuall…
BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
Qingyuan Zhao, Nianqiao Ju, Sergio Bacallado +1
The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation perio…
Selecting and ranking individualized treatment rules with unmeasured confounding
Bo Zhang, Jordan Weiss, Dylan S Small +1
It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is…
ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogenous Variable
Hyunseung Kang, Yang Jiang, Qingyuan Zhao +1
We present a comprehensive R software ivmodel for analyzing instrumental variables with one endogenous variable. The package implements a general class of estimators called k- clas…
Sample-constrained partial identification with application to selection bias
Matthew Tudball, Rachael Hughes, Kate Tilling +2
Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Desp…
Powerful genome-wide design and robust statistical inference in two-sample summary-data Mendelian randomization
Qingyuan Zhao, Yang Chen, Jingshu Wang +1
Two-sample summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS continuing t…
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
Qingyuan Zhao, Jingshu Wang, Gibran Hemani +2
Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in ep…
Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap
Qingyuan Zhao, Dylan S. Small, Bhaswar B. Bhattacharya
To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the "missing at random" and "no unmeasured confounde…
Two-sample instrumental variable analyses using heterogeneous samples
Qingyuan Zhao, Jingshu Wang, Jack Bowden +1
Instrumental variable analysis is a widely used method to estimate causal effects in the presence of unmeasured confounding. When the instruments, exposure and outcome are not meas…
Selective inference for effect modification via the lasso
Qingyuan Zhao, Dylan S. Small, Ashkan Ertefaie
Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision making.…