A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching

This paper introduces a new computational framework for causal inference combining tree-based discretization and integer linear programming-based matching. It aims to accurately uncover causal relationships from observational data while balancing interpretability and computational efficiency, demonstrating practical advantages over existing methods.