This paper provides a critical review of the Bayesian perspective of causal inference based
on the potential outcomes framework. We review the causal estimands, assignment …

The scientific rigor and computational methods of causal inference have had great impacts
on many disciplines but have only recently begun to take hold in spatial applications. Spatial …

This book was written to summarize and describe the state of the art of statistical methods
developed to address questions of estimation and inference for dynamic treatment regimes …

The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we
present a different perspective that focuses on missing observations as the source of …

Although review papers on causal inference methods are now available, there is a lack of
introductory overviews on what they can render and on the guiding criteria for choosing one …

Background The optimal thresholds for the initiation of invasive ventilation in patients with
hypoxemic respiratory failure are unknown. Using the saturation-to-inspired oxygen ratio …

We study Bayesian approaches to causal inference via propensity score regression. Much of
Bayesian methodology relies on parametric and distributional assumptions, with presumed …

In causal inference the effect of confounding may be controlled using regression adjustment
in an outcome model, propensity score adjustment, inverse probability of treatment …

Epidemiologists often wish to estimate quantities that are easy to communicate and
correspond to the results of realistic public health interventions. Methods from causal …

Valid causal inference from observational studies requires controlling for confounders.
When time-dependent confounders are present that serve as mediators of treatment effects …