Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of
flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional …

We propose a novel Bayesian methodology to mitigate misspecification and improve
estimating treatment effects. A plethora of methods to estimate--particularly the …

Teacher shortages and attrition are problems of international concern. One of the most
frequent reasons for teachers leaving the profession is a lack of job satisfaction. Accordingly …

Causal inference practitioners have increasingly adopted machine learning techniques with
the aim of producing principled uncertainty quantification for causal effects while minimizing …

This study proposes a heterogeneous mediation analysis for survival data that
accommodates multiple mediators and sparsity of the predictors. We introduce a joint …

Bayesian additive regression tree (BART) models have seen increased attention in recent
years as a general-purpose nonparametric modeling technique. BART combines the …

Existing methods for regression-based mediation analysis assume that the exposure-
mediator effect, exposure-outcome effect, and mediator-outcome effect are constant across …

Mediation analysis aims at quantifying and explaining the underlying causal mechanism
between an exposure and an outcome of interest. In the context of survival analysis …

In longitudinal observational studies with a time-to-event outcome, a common objective in
causal analysis is to estimate the causal survival curve under hypothetical intervention …

Causal mediation analysis of observational data is an important tool for investigating the
potential causal effects of medications on disease-related risk factors, and on time-to-death …