Sensitivity analysis via stochastic programming

Confounder selection via iterative graph expansion

Confounder selection via iterative graph expansion

We propose a systematic way to select confounders

Almost exact Mendelian randomization

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

Sensitivity Analysis with the $R^2$-calculus

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

The randomization principle in causal inference: A modern look at some old ideas

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 …

Bounds and semiparametric inference in $L^.Inf$- and $L^2$-sensitivity analysis for observational studies

A unified analysis of regression adjustment in randomized experiments

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 shown that this is not always true. To further understand this …

A crash course on causal inference

Multiple conditional randomization tests