Paper

Score-Based Causal Discovery of Latent Variable Causal Models

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arXiv:2605. 20396v1 Announce Type: cross Abstract: Identifying latent variables and the causal structure involving them is essential across various scientific fields.

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Authors: Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang
Released: 2026-05-21 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Score-Based Causal Discovery of Latent Variable Causal Models overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone". Matching terms: latin, under, without, test, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.20396

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