LUCID: Learning Under Confounding for Inference and Discovery in Time Series

arXiv:2609.31315 · cs.LG, stat.ML · Submitted 2026-09-25 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/bloomberg/causal-ts

Terminology

• Benchmark — 17× in this paper · explained in 2 episodes
• Causal Discovery — 16× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Accuracy — 7× in this paper · explained in 1 episode
• Robustness — 7× in this paper · explained in 5 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Core — 3× in this paper · explained in 2 episodes
• Exchangeability — 2× in this paper · explained in 2 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Non-Gaussianity — 1× in this paper · explained in 2 episodes
• Post-Selection — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Spectral Signature — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Efficient Causal Discovery for Autoregressive Time Series

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