Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery
Time-series causal discovery methods rely on assumptions such as stationarity, regular sampling, and bounded temporal dependence. When these assumptions are violated, structure learning can produce confident but misleading causal graphs without warning. We introduce Causal-Audit, a framework that formalizes assumption validation as calibrated risk assessment. The framework computes effect-size diagnostics across five assumption families (stationarity, irregularity, persistence, nonlinearity, and confounding proxies), aggregates them into four calibrated risk scores with uncertainty intervals, and applies an abstention-aware decision policy that recommends methods (e.g., PCMCI+, VAR-based ...
| Authors | Marco Ruiz, Miguel Arana-Catania, David R. Ardila et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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