Latest

• On statistical and causal models associated with acyclic directed mixed graphs
• Counterfactual explainability of black-box prediction models
• A graphical approach to state variable selection in off-policy learning
• On statistical and causal 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• Comment on FDA draft guidance for industry FDA-2019-D-0934: Adjusting for covariates in randomized clinical trials for drugs and biological products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