40 citations · 69 across the 12 of their papers we have counts for
14 papers
Bayesian Nonparametric Causal Inference for High-Dimensional Nutritional Data via Factor-Based Exposure Mapping
Dafne Zorzetto, Zizhao Xie, Julian Stamp +2
Diet plays a crucial role in health, and understanding the causal effects of dietary patterns is essential for informing public health policy and personalized nutrition strategies.…
A Bayesian framework for cost-effectiveness analysis with time-varying treatment decisions
Esteban Fernández-Morales, Emily M. Ko, Nandita Mitra +2
Cost-effectiveness analyses (CEAs) compare the costs and health outcomes of treatment regimes to inform medical decisions. With observational claims data, CEAs must address nonrand…
Considerations for Estimating Causal Effects of Informatively Timed Treatments
Arman Oganisian
Epidemiological studies are often concerned with estimating causal effects of a sequence of treatment decisions on survival outcomes. In many settings, treatment decisions do not o…
Bayesian Sensitivity Analyses for Policy Evaluation with Difference-in-Differences under Violations of Parallel Trends
Seong Woo Han, Nandita Mitra, Gary Hettinger +1
Violations of the parallel trends assumption pose significant challenges for causal inference in difference-in-differences (DiD) studies, especially in policy evaluations where pre…
Untangling Sample and Population Level Estimands in Bayesian Causal Computation
Arman Oganisian
Model-based Bayesian inference for sample and population-level causal estimands has been growing in popularity. This literature routinely emphasizes clear specification of the targ…
Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers
Esteban Fernández-Morales, Arman Oganisian, Youjin Lee
Synthetic control (SC) methods are widely used to estimate the effects of policy interventions, especially those targeting specific geographic regions, referred to as units. These…
causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes
Han Ji, Arman Oganisian
Observational studies are often conducted to estimate causal effects of treatments or exposures on event-time outcomes. Since treatments are not randomized in observational studies…
A Bayesian Framework for Causal Analysis of Recurrent Events with Timing Misalignment
Arman Oganisian, Anthony Girard, Jon A. Steingrimsson +1
Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under two treatments within a define…
Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing
Arman Oganisian, Kelly D. Getz, Todd A. Alonzo +2
We develop a Bayesian semi-parametric model for the estimating the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML…
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…
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,…
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 causal approach to analysis of censored medical costs in the presence of time-varying treatment
Andrew J. Spieker, Arman Oganisian, Emily M. Ko +2
There has recently been a growing interest in the development of statistical methods to compare medical costs between treatment groups. When cumulative cost is the outcome of inter…