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New research details causal effect identification in complex linear SDEs

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A new research paper published on arXiv explores the identifiability of causal effects in linear stochastic differential equations (SDEs) that include reciprocal interactions and latent variables. The study establishes novel graphical conditions for identifying causal effects in systems with directed 2-cycles, which represent reciprocal interactions. Additionally, the paper introduces an instrumental-variable framework to identify direct causal effects under conditions of partial observability, specifically addressing the challenge posed by latent variables. AI

RANK_REASON The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New research details causal effect identification in complex linear SDEs

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The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [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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