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

2026-04-29

AI Summary

This study addresses the bias in causal effect estimation arising from confounding factors and the conflation of correlation with causation in observational data. To tackle this challenge, the authors propose a novel method that integrates tree-based discretization with integer linear programming (ILP) matching. The approach leverages tree structures to produce interpretable discretizations of covariates and employs ILP to achieve globally optimal matching, thereby enhancing both computational efficiency and the accuracy and unbiasedness of average treatment effect on the treated (ATT) estimates. Empirical evaluations demonstrate that the proposed method consistently outperforms state-of-the-art alternatives across multiple benchmark datasets, offering a favorable balance among interpretability, computational efficiency, and estimation performance.

Abstract

Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction between correlation and causation. While recent advances in causal machine learning and matching algorithms have improved estimation accuracy, these methods often face trade-offs between interpretability and computational efficiency. This paper proposes a novel approach that combines a tree-based discretization technique, tailored for causal inference, with an integer linear programming-based matching algorithm. The discretization ensures approximately linear relationships for control datasets within strata, enabling effective matching, while the optimization framework optimizes for global balance. The resulting algorithm yields computational efficiency and less biased ATT estimates compared to state-of-the-art algorithms. Empirical evaluations demonstrate the proposed method's practical advantages over existing techniques in causal inference scenarios.

Problem

• causal inference
• confounding
• interpretability
• computational efficiency
• observational data

Innovation

• causal inference
• tree-based discretization
• integer linear programming
• matching
• computational efficiency