Causal-Inference

A new Bayesian full-information method for life-course Mendelian randomization

We propose a systematic way to select confounders

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

A new approach to sensitivity analysis in linear SEMs using stochastic optimization and the $R^2$-calculus

Randomization is a fundamental principle in causal inference and was first proposed by R A Fisher about a century ago. Although randomization has now been universally adopted in the design of experiments, its role in the analysis of experiments and …