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English(EN) Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variables

新研究详细介绍了复杂线性SDE中的因果效应识别

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新发表在arXiv上的研究论文探讨了在线性随机微分方程（SDEs）中因果效应的可识别性，这些方程包含相互作用和潜变量。该研究为具有定向2循环（代表相互作用）的系统中的因果效应识别建立了新的图条件。此外，该论文引入了一个工具变量框架，在部分可观测的条件下识别直接因果效应，特别是解决了潜变量带来的挑战。 AI

排序理由 该集群包含一篇发表在arXiv上的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新研究详细介绍了复杂线性SDE中的因果效应识别

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该集群包含一篇发表在arXiv上的研究论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ana Leticia Garcez Vicente, Saber Salehkaleybar ·

    Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variables

    arXiv:2610.31310v1 Announce Type: new Abstract: Continuous-time stochastic differential equations (SDEs) provide a natural framework for modeling interacting dynamical systems. A central causal question is whether causal effects among observed variables can be identified from obs…

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