Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions

ArXi:2605.09870v1 Announce Type: cross We propose SVAR-FM (Structural VAR with Flow Matching), a framework for time series causal discovery that treats a physics-based simulator as a mechanical realization of Pearl's do operator. Clamping a variable inside the simulator physically severs confounding paths, producing interventional data by construction. Conditional Flow Matching then learns the nonlinear interventional conditionals.