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CEDAR introduces a constraint-based method for identifying lagged causal edges in sparse autoregressive time series, offering improved interpretability when data are scarce or variables exhibit strong first-order self-dynamics. The approach combines AR(1)-residualized distance correlation screening with targeted conditional-independence tests to prune indirect effects while managing computational complexity efficiently. This work advances the methodology for uncovering dynamic causal structures in machine learning applications involving sequential time-series data.
CEDAR: Causal Edge Discovery for Autoregressive Processes
arXiv:2607.20696v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2607.20696 [cs.LG]
(or arXiv:2607.20696v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.20696 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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