stat.ME · 2026-09-16 · No. 115

Methodology, 2026-09-16.

2 new papers in stat.ME. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Conformal Policy Learning with Distribution-Free Safety Guarantees

    Ying Jin, Naoki Egami

    stat.ME · cs.LG · econ.EM · math.ST · stat.ML

    Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the average outcomes alone may not be sufficient: decision makers may also seek to protect individuals from harm, in line with the Hippocratic principle of ``do no harm.'' In this paper, we propose \textit{conformal policy learning} (CPL), a policy...

    arxiv.org/abs/2609.17296 · PDF

  2. 02

    Causal Discovery via Transformed Low-Rank Quantile Surfaces

    Ryo Kamimura, Thong Pham

    stat.ME · cs.AI · cs.LG · stat.ML

    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:...

    arxiv.org/abs/2609.16931 · PDF

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