Temporal Causal Observability: Separating Persistent Causal State from Perceptual Access

Temporal causal representation learning (CRL) can recover latent causal factors and their dynamics, yet still fail when perceptual access to those factors changes over time: a change in observation need not imply a change in the underlying causal state. We formalize this distinction as Temporal Causal Observability, separating causal-state evolution from time-varying factor-wise perceptual access and studying controlled interventions that alter access while preserving the causal trajectory. For a frozen temporal CRL model, we show that synchronized reference evidence can evaluate the expected benefit of a fixed measurement correction without observing the true state, under a conditional orthogonality assumption. We then establish an identifiability boundary: evaluating the benefit of a fixed correction does not in general identify when that correction should be applied, because the same observation change may arise from either state dynamics or changing perceptual access. This motivates Regime-Marginalized Elimination (RME), which marginalizes latent access regimes instead of making hard observability decisions. Across multiple temporal CRL systems, reduced perceptual access causes substantial degradation despite strong full-observation recovery. On frozen CITRIS representations, recursive RME initialized from a single clean encoding reduces hidden-state error by 22–74% relative to robust median fusion and by 7–34% relative to recursive hard regime decisions under the same evidence and uncertainty-propagation procedure, across all eight representations. A matched structural ablation further shows that recursive maintenance benefits from aligning access regimes with the causal-factor partition learned by the CRL model. In one-step updates, RME also outperforms hard regime decisions on BISCUIT. These results identify temporal causal observability as a distinct post-identification problem: learning a causal state and its dynamics does not determine whether current perceptual evidence should update that state.