115 citations · 182 across the 13 of their papers we have counts for
13 papers
A Bayesian nonparametric approach for causal inference with multiple mediators
Samrat Roy, Michael J. Daniels, Brendan J. Kelly +1
Mediation analysis with contemporaneously observed multiple mediators is an important area of causal inference. Recent approaches for multiple mediators are often based on parametr…
Addressing Positivity Violations in Causal Effect Estimation using Gaussian Process Priors
Yaqian Zhu, Nandita Mitra, Jason Roy
In observational studies, causal inference relies on several key identifying assumptions. One identifiability condition is the positivity assumption, which requires the probability…
Hierarchical Bayesian Bootstrap for Heterogeneous Treatment Effect Estimation
Arman Oganisian, Nandita Mitra, Jason Roy
A major focus of causal inference is the estimation of heterogeneous average treatment effects (HTE) - average treatment effects within strata of another variable of interest such…
A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
Arman Oganisian, Jason A. Roy
Substantial advances in Bayesian methods for causal inference have been developed in recent years. We provide an introduction to Bayesian inference for causal effects for practicin…
Estimating the impact of treatment compliance over time on smoking cessation using data from ecological momentary assessments (EMA)
Yaoyuan Vincent Tan, Donna Coffman, Megan Piper +1
The Wisconsin Smoker's Health Study (WSHS2) was a longitudinal trial conducted to compare the effectiveness of two commonly used smoking cessation treatments, varenicline and combi…
Bayesian Nonparametric Cost-Effectiveness Analyses: Causal Estimation and Adaptive Subgroup Discovery
Arman Oganisian, Nandita Mitra, Jason Roy
Cost-effectiveness analyses (CEAs) are at the center of health economic decision making. While these analyses help policy analysts and economists determine coverage, inform policy,…
Net benefit separation and the determination curve: a probabilistic framework for cost-effectiveness estimation
Andrew J. Spieker, Nicholas Illenberger, Jason A. Roy +1
Considerations regarding clinical effectiveness and cost are essential in comparing the overall value of two treatments. There has been growing interest in methodology to integrate…
Bayesian additive regression trees and the General BART model
Yaoyuan Vincent Tan, Jason Roy
Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mains…
Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions
Chanmin Kim, Corwin M Zigler, Michael J Daniels +2
Pollutant emissions from coal-burning power plants have been deemed to adversely impact ambient air quality and public health conditions. Despite the noticeable reduction in emissi…
A Bayesian Nonparametric Model for Zero-Inflated Outcomes: Prediction, Clustering, and Causal Estimation
Arman Oganisian, Nandita Mitra, Jason Roy
Researchers are often interested in predicting outcomes, conducting clustering analysis to detect distinct subgroups of their data, or computing causal treatment effects. Pathologi…
A semiparametric modeling approach using Bayesian Additive Regression Trees with an application to evaluate heterogeneous treatment effects
Bret Zeldow, Vincent Lo Re, Jason Roy
Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing nonlinearities between an outcome and covariates and interaction among covar…
Outcome identification in electronic health records using predictions from an enriched Dirichlet process mixture
Bret Zeldow, James Flory, Alisa Stephens-Shields +2
We propose a novel semiparametric model for the joint distribution of a continuous longitudinal outcome and the baseline covariates using an enriched Dirichlet process (EDP) prior.…
Bayesian nonparametric generative models for causal inference with missing at random covariates
Jason Roy, Kirsten J Lum, Michael J. Daniels +3
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and…