Causal discovery under mean independence and linearity
Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumption is fragile: shared volatility, common scale effects, or other forms of dependence can cause the methods to recover the wrong causal order, even with infinite data. We introduce the Linear Mean-Independent Acyclic Model (LiMIAM), which replaces full independence with weaker one-sided mean-independence restrictions on the disturbances. Under finite-order consequences of these restrictions, source nodes are generically identifiable, and hence a compatible causal order can be recovered recursively. Our proof is constructive and leads to Direc...
| Authors | Geert Mesters, Alvaro Ribot, Anna Seigal et al. |
|---|---|
| Primary category | stat.ME |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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