用严谨的合成实验评估因果机器学习方法，提升可信度与实用性。
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
- 通过设计严格合成实验评估因果机器学习性能
- 发现现有评估方法无法可靠检验方法鲁棒性
- 为研究者提供可复现的评估原则，适合算法验证者
因果机器学习有望通过结合机器学习预测能力与因果推断理论，革新决策系统。然而，当前方法在机器学习社区中仍鲜有应用，部分原因在于现有实证评估难以判断其可靠性与稳健性。尤其引发争议的是广泛使用合成实验。本文主张，合成实验实为必要且关键，能精准衡量因果机器学习方法的能力。我们批判性回顾现有评估实践，指出其缺陷，并提出一套基于合成数据的严谨实证分析原则。遵循这些原则，可实现全面评估，增强对因果机器学习方法的信任，推动其更广泛应用与真实世界落地。
原文摘要 · Abstract (English)
Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. However, these methods remain underutilized by the broader machine learning community, in part because current empirical evaluations do not permit assessment of their reliability and robustness, undermining their practical utility. Specifically, one of the principal criticisms made by the community is the extensive use of synthetic experiments. We argue, on the contrary, that synthetic experiments are essential and necessary to precisely assess and understand the capabilities of causal machine learning methods. To substantiate our position, we critically review the current evaluation practices, spotlight their shortcomings, and propose a set of principles for conducting rigorous empirical analyses with synthetic data. Adopting the proposed principles will enable comprehensive evaluations that build trust in causal machine learning methods, driving their broader adoption and impactful real-world use.
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