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One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

We study the problem of recovering the parameters of a multivariate Ornstein-Uhlenbeck (OU) process from steady-state observational and interventional data. In many applications, such as large-scale gene perturbation experiments, only stationary "snapshot" measurements are available, making standard stochastic differential equation estimation methods that rely on time-series trajectories inapplicable. We first establish an identifiability result: one intervention per strongly connected component (SCC) of the drift graph suffices to recover all OU process parameters generically up to a global s

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