arXiv:2606. 03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equivalence classes and sensitivity to finite sample sizes.
By Bo Peng, Kaiwen Wu, Sirui Chen, Zhiheng Wang, Yu Qiao, Chaochao Lu
The paper presents a method for partially identifying counterfactual queries without requiring a fully specified causal graph. By exploiting the topological ordering implied by the query itself, the authors transform the identification problem into a linear programming task, enabling bounds on arbitrary counterfactual and nested counterfactual queries. They demonstrate the tightness of these bounds and illustrate the approach on several case studies, showing its usefulness even when causal knowledge is incomplete.
By Eric Rossetto, Alessandro Antonucci
The paper introduces a reference‑free proxy‑judge framework for autoformalization, separating correctness into grounding, statement faithfulness, coverage, and inference. It presents three theoretical results linking proxy error rates to pass‑but‑wrong rates, the impact of false rejections on refinement, and a decomposition of final pass rates. The framework is instantiated with eight proxies and evaluated on ProofNet, miniF2F, e‑SNLI, and ProntoQA, showing that the predictions hold and identifying failure routes.
By Lei Xu, Xin Quan, Andr\'e Freitas
arXiv:2505. 15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making.
By Xin Shu, Shuai Wang, Ang Li
The paper introduces CERTID, a formal identification pipeline that uses the sound and complete causal identification algorithm ID to certify whether a causal effect is identifiable from a given graph and query. CERTID also verifies returned formulas against structural causal models with known interventional distributions, mitigating structural leakage and providing grading guarantees. The authors evaluate three frontier reasoning models on 1,200 certified instances, finding that accuracy is a poor proxy for soundness, with false-claim rates on non-identifiable queries varying by seventeen-fold across models.
By Arman Behnam, Binghui Wang
The paper introduces QXR, an adaptive framework that repairs inconsistent natural‑language claim sets by treating a large language model as a noisy consistency judge. QXR identifies minimal conflicting subsets and removes a small hitting set of claims, thereby preserving the largest coherent subset of the original set. Experiments on benchmarks derived from VitaminC, FEVER, and conflicting‑evidence RAG show that QXR retains substantially more valid claims than all‑at‑once judging while maintaining high precision.
By Paul He, Elke Kirschbaum, Shiva Kasiviswanathan