Efficient Subgroup Analysis via Optimal Trees with Global Parameter Fusion

Thomas M. Menino Convention & Exhibition Center 

Identifying and making statistical inferences on differential treatment effects-commonly known as subgroup analysis in clinical research-is central to precision health. Tree-based recursive partitioning methods are widely used for subgroup analysis due to their interpretability. Nevertheless, these approaches encounter significant limitations, including suboptimal partitions induced by greedy heuristics and overfitting from locally estimated splits. To address these limitations, we propose a fused optimal causal tree method that leverages mixed-integer optimization to facilitate precise subgroup identification. Our approach ensures globally optimal partitions and introduces a parameter-fusion constraint to facilitate information sharing across related subgroups. We provide theoretical guarantees by rigorously establishing out-of-sample risk bounds and comparing them with those of classical tree-based methods. Empirically, our method consistently outperforms popular baselines in simulations. We demonstrate its practical utility through a case study on the Health and Aging Brain Study–Health Disparities dataset, where our approach yields clinically meaningful insights.

Subgroup Analysis

Mixed integer optimization

Parameter Fusion

Optimal Tree 

Section on Statistical Learning and Data Science