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

TL;DR

This paper introduces a new computational framework combining tree-based discretization and ILP-based matching to improve causal inference accuracy and efficiency from observational data.

Contribution

It presents a novel method that enhances causal inference by integrating discretization with optimization-based matching, balancing interpretability and computational efficiency.

Findings

Achieves less biased ATT estimates than existing methods.

Demonstrates practical advantages in empirical evaluations.

Provides computational efficiency in causal inference tasks.

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.