Loscar：用预训练分位数回归扩展 RESIT 框架到位置尺度噪声模型
Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression
一篇因果发现方向的论文：Loscar 把 RESIT 框架推广到 LSNM，用分位数回归套预训练表格模型，多变量场景比 FGLS 强不少。
Loscar 将 RESIT 因果发现框架从加性噪声模型（ANM）扩展到位置尺度噪声模型（LSNM）。它通过分位数回归估计条件均值和尺度，从而可以把预训练表格基础模型作为即插即用估计器。在二变量和多变量因果发现基准上，in-context 分位数回归在灵活函数类下大幅优于可行的广义最小二乘（FGLS）变体，多变量原因场景优势尤其明显。
Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression
Residual-based causal discovery assumes that each variable is generated from its parents and an independent noise source. It estimates this noise and tests its independence from candidate causes. For identifiable functions such as additive-noise models (ANMs) and the broader class of location-scale noise models (LSNMs), this principle identifies the causal direction, as only the causal model admits this independence. Existing LSNM methods rely on parametric assumptions to obtain standardized residuals, risking model misspecification. In contrast, the RESIT-framework allows for nonparametric estimation of residuals to iteratively identify sink nodes, but was proposed for ANMs. To close this gap, we propose Loscar, an extension of RESIT to LSNMs that estimates the conditional mean and scale via quantile regression, enabling pretrained tabular foundation models as plug-in estimators. We compare it with a feasible generalized least squares (FGLS) variant, which is consistent given suitable basis functions. Across bivariate and multivariate causal-discovery benchmarks, in-context quantile regression substantially outperforms FGLS with flexible function classes, particularly for multivariate causes.