The paper introduces a method called OptABA-PC that treats the repair of conflicting conditional‑independence tests in constraint‑based causal discovery as a minimum‑cost correction problem. Implemented in Answer Set Programming, it provides sound and complete guarantees, ensures subset‑minimality and cost optimality of the repair, and offers conflict‑based justifications for released facts. Empirical results show that OptABA-PC retains more correct facts, reduces ambiguity, and improves graph reconstruction compared to previous Causal ABA approaches.
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.
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.
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.
arXiv:2505. 15274v4 Announce Type: replace Abstract: Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making.
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.
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.
The paper tackles selecting a cost‑constrained set of experiments that most effectively tighten bounds on a partially identifiable causal query. It formalizes this as the NP‑hard max‑potency problem, introduces efficient graphical pruning rules to reduce the search space, and demonstrates the approach on synthetic graphs and real NHANES data to estimate the effect of physical activity on diabetes.
arXiv:2602. 16481v2 Announce Type: replace Abstract: Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions.
arXiv:2607. 13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises.
MuPlon is a new framework for claim verification that models claims and evidence as a fully connected Claim‑Evidence Graph. It tackles two confounding problems—data noise and data biases—by applying a dual causal intervention strategy: a back‑door path that adjusts node weights to reduce noise and strengthen relevant connections, and a front‑door path that extracts key subgraphs, builds reasoning paths, and uses counterfactual reasoning to remove biases. Experiments show that MuPlon surpasses existing methods and achieves state‑of‑the‑art performance.
arXiv:2606. 02326v1 Announce Type: new Abstract: Hard constraints are usually treated as terminal vetoes: once a candidate violates a requirement, the learned rule rejects it and any repair is handled outside the decision semantics.
arXiv:2402.03941v4 Announce Type: replace-cross Abstract: Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress...