Subgroup Analysis

Subgroup analysis aims to identify distinct groups within a dataset that exhibit different responses to treatments or possess unique characteristics, enabling more precise predictions and personalized interventions. Current research emphasizes developing interpretable models, such as rule-based systems and mixture models, to analyze both randomized and observational data, addressing challenges like confounding biases and ensuring reliable uncertainty quantification. This work is crucial for improving the accuracy and trustworthiness of machine learning in diverse fields, from healthcare (personalizing treatments) to radiotherapy (optimizing dose delivery), ultimately leading to more effective and targeted strategies.