The authors propose TTCD (Transformer Integrated Temporal Causal Discovery), an end-to-end framework for learning causal graphs from non-stationary time series. The method combines a Non-Stationary Feature Learner—using temporal and frequency-domain attention plus dynamic non-stationarity profiling—with a Causal Structure Learner that operates on "distilled" reconstructed signals from the transformer decoder. The key innovation is reconstruction-guided causal signal distillation, which filters out noise and spurious correlations while preserving meaningful dependencies. Experiments on synthetic, benchmark, and real-world datasets show TTCD outperforms state-of-the-art causal discovery baselines in accuracy and consistency with domain knowledge.
Main takeaways:
- Existing causal discovery methods struggle with non-stationary, nonlinear, noisy time series due to reliance on conditional independence tests or strong statistical assumptions.
- TTCD learns both contemporaneous (same-timestep) and lagged (across-timestep) causal relationships without restrictive assumptions on noise distributions.
- Reconstruction-guided distillation uses the transformer decoder's reconstruction process to filter noise and spurious correlations, isolating causal signals.
- The Causal Structure Learner infers the causal graph from these distilled signals, avoiding brittle conditional independence tests.
- The method consistently outperforms baselines on synthetic, benchmark, and real-world datasets, including better alignment with domain knowledge.