The growing interest in causal inference in recent years has led to new causal inference
methodologies and their applications across disciplines and research domains. Yet, studies …

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

In this work, we examine recently developed methods for Bayesian inference of optimal
dynamic treatment regimes (DTRs). DTRs are a set of treatment decision rules aimed at …

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 …

This paper quantifies the effect of speed cameras on road traffic collisions using an
approximate Bayesian doubly-robust (DR) causal inference estimation method. Previous …

The two‐stage process of propensity score analysis (PSA) includes a design stage where
propensity scores (PSs) are estimated and implemented to approximate a randomized …

Most previous studies investigate the safety effects of a single speed camera, ignoring the
potential impacts from adjacent speed cameras. The mutual influence between two or even …

This paper estimates the causal effect of 20 mph zones on road casualties in London.
Potential confounders in the key relationship of interest are included within outcome …

Statistical evaluation of road safety interventions can be undertaken using a variety of
different approaches, typically requiring different assumptions to obtain causal identification …