May 14, 2024, 4:41 a.m. | Kang Du, Yu Xiang

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.06902v1 Announce Type: new
Abstract: Causal inference from observational data following the restricted structural causal models (SCM) framework hinges largely on the asymmetry between cause and effect from the data generating mechanisms, such as non-Gaussianity or non-linearity. This methodology can be adapted to stationary time series, yet inferring causal relationships from nonstationary time series remains a challenging task. In this work, we propose a new class of restricted SCM, via a time-varying filter and stationary noise, and exploit the asymmetry …

abstract arxiv causal causal inference cause and effect cs.lg data framework inference methodology processes relationships scm series stat.ml time series type

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