A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching
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 l...
| Authors | Tianyu Yang, Md. Noor-E-Alam |
|---|---|
| Primary category | stat.ML |
| Published | 30 April 2026 |
| Last updated at source | 6 May 2026 |
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