Mask2Cause: Temporal Causal Discovery Beyond Causality in Mean
One approach to discovering causal relationships in multivariate time series is to ask whether a variable's history improves prediction of another variable. When this improvement is measured solely by reductions in optimal squared error, the criterion misses relationships that affect the target's conditional variance without changing its conditional mean. We propose Mask2Cause, an end-to-end Transformer framework for causal discovery through conditional mean and variance prediction. Each variable's history is embedded as a separate token, and a single learnable adjacency matrix, shared across layers and attention heads, controls information exchange between tokens. Gaussian negative log-l...
| Authors | Omar Muhammad, Pasupuleti Dhruv Shivkant, Deepak N. Subramani |
|---|---|
| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 3 October 2026 |
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