I am a Professor of Statistics in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics (DPMMS) at University of Cambridge, a Fellow of the Corpus Christi College, and an Associate Faculty of the Cambridge Centre for AI in Medicine (CCAIM).
I am interested in improving the general quality and appraisal of statistical research, including new methodology and a better understanding of causal inference, novel study designs, sensitivity analysis, multiple testing, and selective inference.
Click here for my name in Chinese and how to pronounce it.
Interests
- Causal inference
- Selective inference
- Applied statistics
Education
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PhD in Statistics, 2016
Stanford University
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BSc in Mathematics, 2011
University of Science and Technology of China (USTC)
Latest
- On statistical models associated with acyclic directed mixed graphs
- Acyclic Directed Mixed Graphs: Matrix Algebra, Statistical Models, Confounder Selection
- Causal Perspectives on 'Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models' by Apley and Zhu
- Post-selection inference for effect modification
- A matrix algebra for graphical statistical models
- Selective Randomization Inference for Adaptive Experiments
- Design: The Elusive Principle of Statistics
- Sensitivity analysis for observational studies: past, present, and future
- Causal mediation analysis for time-varying heritable risk factors with Mendelian Randomization
- Confounder selection via iterative graph expansion
- A constructive approach to selective risk control
- Sensitivity analysis via stochastic programming
- Confounder selection via iterative graph expansion
- Confounder selection via iterative graph expansion
- Using Mendelian randomization to discover biological mechanisms
- Almost exact Mendelian randomization
- Simultaneous hypothesis testing using negative controls
- Simultaneous Hypothesis Testing Using Internal Negative Controls with An Application to Proteomics
- Sensitivity Analysis with the $R^2$-calculus
- The randomization principle in causal inference: A modern look at some old ideas
- Bounds and semiparametric inference in $L^.Inf$- and $L^2$-sensitivity analysis for observational studies
- A unified analysis of regression adjustment in randomized experiments
- A crash course on causal inference
- Multiple conditional randomization tests
- Confounder selection: Objectives and approaches
- Almost exact Mendelian randomization
- Multiple conditional randomization tests
- Two High-Profile Examples of Selection Bias
- PathGPS: Discover shared genetic architecture using biobank data
- Fisher, Statistics, and Randomization
- What is a randomization test?
- How infectious was COVID-19 when it first circulated in Wuhan?
- What is a randomization test?
- Mendelian randomization
- Leverage Mendelian Randomization to Learn Meaningful Representations (LMR×2)
- Multiple conditional randomization tests
- Small Data, Big Time—A retrospect of the first weeks of COVID-19
- Comment on FDA draft guidance for industry FDA-2019-D-0934: Adjusting for covariates in randomized clinical trials for drugs and biological products
- Multiple conditional randomization tests for lagged and spillover treatment effects
- Small Data, Big Time—A retrospect of the first weeks of COVID-19
- On testing mean proportionality of multivariate normal variables
- Discussion on 'Causal Inference in Genetic Trio Studies'
- Selecting and ranking individualized treatment rules with unmeasured confounding
- Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
- Selection bias in 2020
- Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials
- A note on post-treatment selection in studying racial discrimination in policing
- Mendelian randomization with coarsened exposures
- Letter to the editor: Generation interval for COVID-19 based on symptom onset data
- A latent mixture model for heterogeneous causal mechanisms in Mendelian randomization
- Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments
- BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
- ivmodel: An R package for inference and sensitivity analysis of instrumental variables models with one endogenous variable
- Profile-likelihood Bayesian model averaging for two-sample summary data Mendelian randomization in the presence of horizontal pleiotropy
- Analysis of the epidemic growth of the early 2019-nCoV outbreak using internationally confirmed cases
- Sample-constrained partial identification with application to selection bias
- A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease
- Selecting and ranking individualized treatment rules with unmeasured confounding
- Comment: Will competition-winning methods for causal inference also succeed in practice?
- Selective inference for effect modification: An empirical investigation
- Performance evaluation with latent factors
- Falsification tests for instrumental variable designs with an application to tendency to operate
- Graphical diagnosis of confounding bias in instrumental variables analysis
- Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization
- Improving the accuracy of two-sample summary data Mendelian randomization: moving beyond the NOME assumption
- Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
- Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap
- Two-sample instrumental variable analyses using heterogeneous samples
- Selective inference for effect modification via the lasso
- Multiple testing when many $p$-values are uniformly conservative
- Cross-screening in observational studies that test many hypotheses
- Estimation and prediction in sparse and unbalanced tables
- On sensitivity value of pair-matched observational studies
- Causal interpretations of black-box models
- Comment on 'Causal inference using invariant prediction'
- Permutation $p$-value approximation via generalized Stolarsky invariance
- Covariate balancing propensity score by tailored loss functions
- Confounder adjustment in multiple hypothesis testing
- SEISMIC: A self-exciting point process model for predicting tweet popularity
- Entropy balancing is doubly robust