Causal Discovery via Transformed Low-Rank Quantile Surfaces
We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the conditional quantile surface admits a low-rank functional decomposition. LRQS subsumes location-scale noise models and post-nonlinear heteroscedastic noise models, while allowing multiple quantile bases to represent changes beyond location-scale effects. We prove generic identifiability of LRQS: the transformed quantile surface is low rank in the causal direction, whereas reverse representability under the corresponding constraints occurs only for exceptional, fine-tuned cause marginals. We provide a simple-yet-powerful causal score using a no...
| Authors | Ryo Kamimura, Thong Pham |
|---|---|
| Primary category | stat.ME |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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