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English(EN) Mining Causality: AI-Assisted Search for Instrumental Variables

新研究利用大型语言模型和新算法推进因果发现 · 跟踪4个来源

作者 PulseAugur 编辑部 · [4 个来源] ·

近期研究探索了因果发现的先进技术，该领域专注于从数据中推断因果关系。一篇论文研究了用于监督因果发现的模拟数据中嵌入的假设，强调了这些假设如何影响可识别性和泛化能力。另一项研究介绍了一种名为DISCO的方法，通过调整离散分布的评分匹配技术，实现了从计数数据中进行因果发现。此外，一篇论文提出使用大型语言模型来辅助寻找经济学中因果推断的工具变量，加速了传统上启发式的方法。最后，提出了一种名为MARCEDES的新型基于评分的方法，用于通过连续优化学习具有非高斯误差的因果结构。 AI

影响 因果发现方法的进步，特别是那些利用大型语言模型和针对离散及非高斯数据的新算法的方法，可能显著提高AI理解和建模复杂现实世界系统的能力。

排序理由 该集群包含多篇在arXiv上发表的学术论文，详细介绍了因果发现中的新方法和理论探索。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

新研究利用大型语言模型和新算法推进因果发现 · 跟踪4个来源

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该集群包含多篇在arXiv上发表的学术论文，详细介绍了因果发现中的新方法和理论探索。
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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Pingchuan Ma, Rui Ding, Bojun Huang, Shuai Wang ·

    揭秘监督因果发现中的数据和模拟器假设

    arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pairs are typically simulated, making the simulator both a source of supervision and…

  2. arXiv stat.ML TIER_1 English(EN) · Euijong Song, Hyewon Park, Gunwoong Park ·

    离散分数匹配实现从计数数据进行因果发现

    arXiv:2609.39326v1 Announce Type: new Abstract: Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences does not generally suffice for causal discovery. We generalize SCORE's constant-cu…

  3. arXiv stat.ML TIER_1 English(EN) · Sukjin Han ·

    挖掘因果关系：AI 辅助寻找工具变量

    arXiv:2409.14202v4 Announce Type: replace-cross Abstract: The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying their validity---especially exclusion restrictions---is largely …

  4. arXiv stat.ML TIER_1 English(EN) · Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni ·

    MARCEDES：非高斯分布下的基于分数的因果发现与连续优化

    arXiv:2609.30643v1 Announce Type: new Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors. Motivated by an intentionally misspecified non-Gaussian SE…

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