2026
On the identifiability of causal graphs with multiple environments
ICLR 2026poster
Causal discovery from i.i.d. observational data is known to be generally ill-posed. We demonstrate that if we have access to the distribution induced by a structural causal model, and additional data from (in the best case) *only two* environments that sufficiently differ in the noise statistics, t…