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

Confounder selection: Objectives and approaches

We provide a unified review of various confounder selection criteria in the literature and the assumptions behind them.

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.

Multiple conditional randomization tests

Amusing counterfactual inference (by words)

My good friend Joshua Loftus and I spent some 30 minutes to crack (at least we think we did!) a counterfactual inference made in a speech in the House of Commons in London in 1850 by Lord Palmerston, who was the Secretary of State for Foreign Affairs at the time.

What is a randomization test?

The meaning of randomization tests has become obscure in statistics education and practice over the last century. This article makes a fresh attempt at rectifying this core concept of statistics. A new term---'quasi-randomization test'---is …