Current Research

• Aggregate Bayesian Causal Forests (aBCF) for identifying high-performing primary care practices.
• Bayesian hierarchical modeling for small-sample impact estimation.
• Simulation studies optimizing trade-offs between covariate diversity and power.
• Real-world data analyses using augmented synthetic controls and Bayesian time series models.

Methodological Interests

• Bayesian causal inference and hierarchical modeling
• Simulation-based study design
• Probabilistic modeling and uncertainty quantification
• Applications in health policy and clinical outcomes