By combining causal graphs and randomization inference, a formal justification for Mendelian randomization is given in the context of with-family studies.

Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (“negative …

Randomization is a fundamental principle in causal inference and was first proposed by R A Fisher about a century ago. Although …

This talk will examine the selection bias that occurred in studying some most contentious problems. In the first case study, we will …

One of Fisher’s most important scientific contributions is the paradigm of randomized experiments. I will try to trace the origin of …

This is given to A-level students.

Two central objectives of individualized treatment are precision and optimality. A third objective is robustness, and this talk aims to …

The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early …

Mendelian randomization (MR) is a method for causal inference that utilizes the natural experiment in genetic inheritance. The idea of …

Mendelian randomization (MR) is a method for causal inference that utilizes the natural experiment in genetic inheritance. The idea of …

Discussion on Dr Stephen Bates’ talk in the Online Causal Inference Seminar.

This talk will examine the selection bias that occurred in studying some most contentious problems in 2020. In the first case study, we …

Mendelian Randomization (MR) is a popular method in epidemiology and genetics that uses genetic variation as instrumental variables for …

Sensitivity analysis is widely recognized as a critical step in an observational study but is seldom found in applications. One reason …

Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. …

Introduction to causal inference for social science PhD students.

More than fifty years ago, John Tukey first envisioned a field we now call “Data Science” (he called it “Data …

Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. …

This report entered the 2019 MR Data Challenge and contains reproducible R code for our analysis.

Invited talk giving an overview of my research on summary-data Mendelian randomization.

Tutorial talk for the theory, methods, and practice of Mendelian randomization.

2024

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