Many statistical methods (estimators) are available for estimating the consensus value (or
average effect) and heterogeneity variance in interlaboratory studies or meta‐analyses …

Machine learning is increasingly used to discover diagnostic and prognostic biomarkers
from high-dimensional molecular data. However, a variety of factors related to experimental …

Propensity score (PS) methods are popular when estimating causal effects in non-
randomized studies. Drawing causal conclusion relies on the unconfoundedness …

Meta-analysis is a statistical method used in systematic review to quantitatively integrate the
results of multiple related studies to obtain a pooled result that can represent these studies …

We propose a doubly robust approach to characterizing treatment effect heterogeneity in
observational studies. We develop a frequentist inferential procedure that utilizes posterior …

We propose a model averaging approach, combined with a partition and matching method
to estimate the conditional average treatment effects under heteroskedastic error settings …

This chapter introduce readers to the different ways in which Bayesian inference has been
successfully applied to causal inference problems. It focuses on propensity score estimation …