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Causal Inference with Categorical Unobserved Confounder via Mixture Learning

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arXiv:2605. 19006v1 Announce Type: cross Abstract: Unobserved confounding is a fundamental challenge for estimating causal effects.

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Authors: Aytijhya Saha, Stephen Bates, Devavrat Shah
Released: 2026-05-20 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Causal Inference with Categorical Unobserved Confounder via Mixture Learning overlaps with 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)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH 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: under, rate, control, position. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.19006

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