用物理模拟器做因果推断，解决时间序列混淆问题。
Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
- 用模拟器物理干预变量，生成真实干预数据。
- 在四个科学领域验证，可正确识别因果方向，观测法会出错。
- 首次发现模拟精度下降会导致因果方向反转，适合高精度仿真研究者。
我们提出SVAR-FM（带流匹配的结构化向量自回归），一种基于物理模拟器的时间序列因果发现框架，将模拟器视为佩尔因果干预算子（do算子）的物理实现。通过在模拟器中固定某个变量，可物理切断混杂路径，直接生成干预数据。条件流匹配用于学习非线性干预条件分布。理论上，我们在模拟器可干预变量满足覆盖条件下，证明了全结构向量自回归模型的可识别性，并导出了端到端误差界，该界分解为蒙特卡洛、模拟器保真度和流匹配三部分。一个符号翻转推论表明：当模拟器精度低于阈值时，估计的因果效应符号会反转。实证上，在四个科学领域基准测试中，SVAR-FM成功恢复了正确因果符号，而观测方法因混杂导致符号反转。在超快激光物理的案例研究中，通过物理调节第一性原理量子求解器的精度，验证了符号翻转预测：低精度设置下因果符号反转，高精度设置下恢复正确方向（R² = 0.983，无偏差）。
原文摘要 · Abstract (English)
We propose SVAR-FM (Structural VAR with Flow Matching), a framework for time series causal discovery that treats a physics-based simulator as a mechanical realization of Pearl's do operator. Clamping a variable inside the simulator physically severs confounding paths, producing interventional data by construction. Conditional Flow Matching then learns the nonlinear interventional conditionals. Theoretically, we prove that the full structural VAR becomes identifiable under a coverage condition on the simulator-clampable variables, and derive an end-to-end error bound that decomposes into Monte Carlo, simulator fidelity, and Flow Matching terms. A sign-flip corollary predicts that when simulator accuracy falls below a threshold, the estimated causal effect reverses sign. Empirically, a benchmark across four scientific domains confirms that SVAR-FM recovers the correct causal sign where observational methods produce sign-reversed estimates due to confounding. A case study in ultrafast laser physics verifies the sign-flip prediction by physically varying the accuracy level of a first-principles quantum solver: the low-accuracy setting reverses the causal sign, while the high-accuracy setting recovers the correct direction (R-squared = 0.983, zero bias).
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