Enhancing Efficiency Analysis Trees: A Hybrid Approach Using Heuristics and Backtracking Introduction In an era where data is key to optimizing performance across industries, the measurement of technical efficiency remains a crucial task in economics, engineering, and operations research. Traditional methods such as Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) have long been used for estimating production frontiers, but new approaches rooted in machine learning are pushing the boundaries. One such technique is the Efficiency Analysis Tree (EAT)—a decision tree-based, nonparametric method designed to model production frontiers. While EAT has proven effective, recent research highlights opportunities for improvement, particularly in balancing accuracy and computational efficiency. This blog explores a novel solution presented in the paper “Heuristic and Backtracking Algorithms for Improving the Performance of Efficiency Analysis Trees” by Esteve et ...