On the identifiability of causal graphs with multiple environments

ArXi:2510.13583v3 Announce Type: replace-cross Causal discovery from i.i.d. observational data is known to be generally ill-posed. We nstrate that if we have access to the distribution {induced} by a structural causal model, and additional data from (in the best case) \textit{only two} environments that sufficiently differ in the noise statistics, the unique causal graph is identifiable. Notably, this is the first result in the literature that guarantees the entire causal graph recovery with a constant number of environments and arbitrary nonlinear mechanisms.