发表机构
Bloomberg LP（彭博有限合伙企业）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究稀疏自回归时间序列滞后因果边发现问题，提出CEDAR方法，通过AR(1)残差化等筛选候选滞后，经条件独立性测试、MCI修剪等确定因果边，在数据稀缺等情况效果好，兼具边级可解释性。
AI 中文摘要
我们提出了CEDAR（自回归过程的因果边发现），这是一种基于约束的方法，用于在稀疏自回归时间序列中发现滞后因果边。CEDAR使用AR(1)残差化、U中心距离相关性筛选候选交叉变量滞后，然后对每个显著的交叉变量滞后候选应用两个有针对性的条件独立性测试，每个有序对最多接受一个滞后。一个稳定的MCI修剪步骤去除间接边，可选的确定性C节点针对指定的趋势类非平稳性进行调整。在筛选后很少有滞后存活的稀疏情况下，CEDAR在筛选后需要$O(d^2)$个CI测试，同时保留边级可解释性。当数据稀缺且变量表现出滞后1自动态时，CEDAR最有效；随着$T$的增长或当高阶自回归或同时多滞后效应常见时，具有更丰富条件集的方法变得更可取。
英文摘要
We propose CEDAR (Causal Edge Discovery for Autoregressive Processes), a constraint-based method for lagged causal edge discovery in sparse autoregressive time series. CEDAR screens candidate cross-variable lags using AR(1)-residualized, U-centered distance correlation, then applies two targeted conditional-independence tests per significant cross-variable lag candidate and accepts at most one lag per ordered pair. A stable MCI pruning step removes indirect edges, and optional deterministic C-nodes adjust for specified trend-like nonstationarity. In sparse regimes where few lags survive screening, CEDAR requires $O(d^2)$ CI tests after screening while retaining edge-level interpretability. CEDAR is most effective when data are scarce and variables exhibit lag-1 self-dynamics; methods with richer conditioning sets become preferable as $T$ grows or when higher-order autoregressive or simultaneous multi-lag effects are common.
Comments8 main pages, 5 figures
