Paper

Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

Unreadunread

AI summary

arXiv:2606. 07399v1 Announce Type: new Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification.

Reading status

Priority

About this paper

Authors: Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina
Released: 2026-06-08 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions 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)", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone", experiment "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: under, control, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.07399

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.