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 …

Bayesian inference is a powerful tool for combining information in complex settings, a task of
increasing importance in modern applications. However, Bayesian inference with a flawed …

While statistical analysis of a single network has received a lot of attention in recent years,
with a focus on social networks, analysis of a sample of networks presents its own …

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

A Gaussian process (GP) covariance function is proposed as a matching tool for causal
inference within a full Bayesian framework under relatively weaker causal assumptions. We …

Many spatial phenomena exhibit interference, where exposures at one location may affect
the response at other locations. Because interference violates the stable unit treatment value …

We introduce a framework for estimating causal effects of binary and continuous treatments
in high dimensions. We show how posterior distributions of treatment and outcome models …

Considerable statistical work done on dynamic treatment regimes (DTRs) is in the
frequentist paradigm, but Bayesian methods may have much to offer in this setting as they …

Abstract Bayesian Additive Regression Trees (BART) is a flexible machine learning
algorithm capable of capturing nonlinearities between an outcome and covariates and …