Paper

Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls

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arXiv:2606. 08941v1 Announce Type: new Abstract: This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization over some variables in completed partially directed acyclic graphs (CPDAGs).

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Authors: Yuxin Deng, Yi Sun, Zhiming Li, Huaxiong Liu
Released: 2026-06-09 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", 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)". Matching terms: code, strong, rect. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.08941

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