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

arXiv stat.ML / 5/1/2026

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Key Points

The paper addresses the difficulty of causal inference from observational data, where confounding can blur the line between correlation and causation.
It introduces a framework that combines tree-based discretization designed for causal inference with an ILP-based matching method to obtain better global balance.
The discretization step aims to make relationships approximately linear within strata (for control datasets), improving the effectiveness of matching.
Experimental results suggest the approach is more computationally efficient and produces less biased ATT estimates than several state-of-the-art causal inference algorithms.

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.