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arXiv Machine Learning By Daniel Klippert, Quang Nhat Huynh, Alexander Marx

Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression

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Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression proposes Loscar, an extension of the RESIT framework to location-scale noise models (LSNMs). Loscar estimates conditional mean and scale using quantile regression, allowing pretrained tabular foundation models to serve as plug‑in estimators. Experiments on bivariate and multivariate causal‑discovery benchmarks show that in‑context quantile regression outperforms a feasible generalized least squares (FGLS) variant, especially when causes are multivariate.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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