Abstract:Causal discovery, the problem of inferring the direction of causality, is generally ill-posed. We use the language of structural causal models (SCM) to show that assuming that the causal relations are acyclic and invariant across multiple environments (e.g., the way minimum wage affects employment rate is stable across different geographical regions), \textit{only} two auxiliary environments are sufficient to infer the causal graph for arbitrary nonlinear mechanisms. Moreover, we demonstrate that this implies identifiability of the SCM functional mechanisms: as a corollary, we show that \textit{two} auxiliary environments are sufficient to guarantee correct counterfactual inference. We empirically support our theoretical results on synthetic data.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13589 [stat.ML] |
| (or arXiv:2605.13589v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13589 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Francesco Montagna [view email]
[v1]
Wed, 13 May 2026 14:25:04 UTC (299 KB)