构建真实产线的因果模拟器，用于评测因果推断方法
CausalMan: A physics-based simulator for large-scale causality
- 基于真实产线设计物理驱动的因果模拟器
- 揭示主流方法在复杂场景下的性能差距与计算瓶颈
- 提供两个数据集，适合因果学习与算法评测研究者
深入理解因果关系对驾驭当今复杂现实系统至关重要。缺乏具有已知数据生成过程的真实因果模型，使得公平基准测试变得困难。本文提出CausalMan模拟器，其结构模仿真实生产流水线，包含多种线性和非线性机制以及难以预测的行为（如离散模式切换）。我们展示了众多先进方法的不足，并分析了它们在运行时间和内存复杂度上的显著差异。作为贡献，我们将发布CausalMan大规模模拟器，提供两个衍生数据集，并进行了全面评估。
原文摘要 · Abstract (English)
A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known data generating processes complicates fair benchmarking. In this paper, we present the CausalMan simulator, modeled after a real-world production line. The simulator features a diverse range of linear and non-linear mechanisms and challenging-to-predict behaviors, such as discrete mode changes. We demonstrate the inadequacy of many state-of-the-art approaches and analyze the significant differences in their performance and tractability, both in terms of runtime and memory complexity. As a contribution, we will release the CausalMan large-scale simulator. We present two derived datasets, and perform an extensive evaluation of both.
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