用树状分段+整数规划匹配，提升因果推断的效率和准确度
A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching
- 通过树状分段将数据分层，使每层内变量关系近似线性
- 整数规划优化全局平衡，显著降低处理效应估计偏差
- 兼顾可解释性与计算效率，适合实际决策场景
因果推断对数据驱动决策至关重要，旨在从观测数据中发现因果关系。然而，混杂因素的存在以及相关性与因果性的区别使得因果识别仍具挑战。尽管近年来因果机器学习和匹配算法有所进展，但常在可解释性与计算效率间权衡。本文提出一种新方法：结合树状分段技术（专为因果推断设计）与基于整数线性规划（ILP）的匹配算法。分段确保各层内控制组数据关系近似线性，有利于有效匹配；优化框架则实现全局平衡。实验表明，该方法在计算效率和平均处理效应（ATT）估计偏差方面优于现有先进算法，在多种因果推断场景中展现实用优势。
原文摘要 · Abstract (English)
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.
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