Expanding the Application of Propensity Scores: A Study of Multi-Treatment Matching Package

Music City Center 

Nonrandomized studies often suffer from confounding, as the lack of random assignment necessitates statistical techniques to approximate controlled experiments for valid causal inference. If confounding is mishandled studies may falsely attribute the effect of a confounder to exposure thus incorrect conclusions. To improve causal inference, the studies must mimic randomized controlled trials to ensure valid comparisons between treated and untreated groups. Propensity score methods are widely used to mitigate confounding by balancing covariates. While traditional approaches focus on binary treatments, multi-treatment settings introduce complexities in estimation and matching. This research develops a novel algorithm and R package for multi-treatment propensity score matching, integrating logistic regression, machine learning, and advanced matching methods. We evaluate performance across varying data structures and confounding levels using simulated and real-world datasets, measuring balance diagnostics, bias reduction, and treatment effect. These findings advance multi-treatment propensity score methods, offering a more robust framework for causal inference in observational studies

Propensity Scores

Matching

Machine Learning

Treatment Comparison

Multivariate Regression

Balancing 

Quantum Computing in Statistics and Machine Learning Interest Group