arXiv AI By Cong Cao

When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation

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arXiv Machine Learning
Oct 22

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

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.

By Daniel Klippert, Quang Nhat Huynh, Alexander Marx
arXiv Machine Learning
Oct 19

Unit-level aggregation breaks coverage in causal foundation models

The paper investigates why causal foundation models (CFMs) produce poorly calibrated credible intervals when aggregating unit-level estimates over groups. It shows that CFMs combine unit-level variances assuming independence, but posterior uncertainty induces correlations that shrink the combined variance, leading to coverage loss that worsens with group size. The authors demonstrate that neural process models, which jointly predict for groups, correct this bias and markedly improve interval coverage across synthetic and benchmark datasets.

By Gabe Schulman, Lucius E. J. Bynum, Yoav Wald