发表机构
University of California, Los Angeles; University of Oxford; Oxford-Man Institute of Quantitative Finance（加州大学洛杉矶分校; 牛津大学; 牛津-曼氏定量金融研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ORACLE-VARX方法，结合去偏机器学习、自适应滞后估计和FDR控制的检验，在混杂时间序列中发现因果滞后结构，并在合成与真实数据上优于现有方法。
AI 中文摘要
在多变量时间序列中找出哪些变量导致其他变量以及滞后多少期，对科学和政策至关重要，然而现有方法不得不在灵活的混杂调整、数据驱动的滞后选择以及控制错误发现率（FDR）的推断之间做出取舍。ORACLE-VARX在一条流程中同时实现了这三者。首先，双重/去偏机器学习（DML）从结果和滞后序列中去除非线性混杂效应。其次，自适应因果滞后估计（ACLE）通过序贯显著性检验在每个时间步选择滞后阶数，并跟踪机制变化。第三，采用Benjamini-Hochberg校正的逐元素$z$检验在目标FDR下选择有向边。我们证明，在每个滚动窗口中，去偏系数围绕窗口平均目标渐近正态，因此其$z$检验渐近有效。在具有时变结构和非线性混杂的合成基准上，ORACLE-VARX（LightGBM）最佳地追踪真实滞后阶数（RMSE $0.96$ 对比 $1.1$--$1.5$），边FDR为$0.047$，接近PCMCI（$0.045$）且低于VAR（$0.129$）和VAR-LiNGAM（$0.187$），并且预测优于所有三者。在具有宏观经济混杂的九个美国行业ETF上，它产生了可解释的因果图，其滞后阶数在高波动机制中上升。
英文摘要
Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force a choice between flexible confounder adjustment, data-driven lag selection, and inference that controls the false discovery rate (FDR). ORACLE-VARX does all three in one pipeline. First, double/debiased machine learning (DML) removes nonlinear confounder effects from the outcomes and the lagged series. Second, adaptive causal lag estimation (ACLE) picks the lag order at each time step by sequential significance tests, tracking regime changes. Third, entry-wise $z$-tests with Benjamini--Hochberg correction select directed edges at a target FDR. We prove that in each rolling window, the debiased coefficients are asymptotically normal around a window-averaged target, so their $z$-tests are asymptotically valid. On a synthetic benchmark with time-varying structure and nonlinear confounding, ORACLE-VARX (LightGBM) tracks the true lag order best (RMSE $0.96$ vs $1.1$--$1.5$), has edge FDR $0.047$, close to PCMCI ($0.045$) and below VAR ($0.129$) and VAR-LiNGAM ($0.187$), and forecasts better than all three. On nine U.S. sector ETFs with macroeconomic confounders, it yields interpretable causal graphs whose lag order rises in high-volatility regimes.
Comments48 pages, https://github.com/HK-Tan/ORACLE-VARX
