iASIDE: Identifiable, Adaptable, and Separable Interventional Dynamics

Learning latent causal dynamics offers an important approach for predicting a dynamics system's response to hypothetical external interventions, especially in systems with high-dimensional observations. This presents two related but distinct challenges: identifiability of the intervention effect in the presence of potential intervention assignment bias, and identifiability of the latent dynamics process that is not directly observed. While the latter challenge persists regardless of the former, it has received limited attention in existing works of intervention response prediction. We bridge this gap with iASIDE, a framework for attaining *identifiable*, *adaptable* and *separable* interventional dynamics. We formulate a latent process with non-stationary parameters and establish its *theoretical* identifiability by conditioning its non-stationary parameters on the variable history. We then address the *algorithmic* challenge of reaching the optimal identifiable solution — caused by the difficulty of separating native and response dynamics from their composite observations — by a separable formulation for both the latent dynamics and the conditional priors of its non-stationary parameters. We realize amortized variational inference of this latent dynamics with feed-forward meta-learning. Experiments on synthetic and real data showed that iASIDE significantly improved the accuracy of intervention response predictions, especially over increased prediction horizons and data heterogeneity.