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Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains

A. Theocharous · G. G. Gregoriou · P. Sapountzis · I. Kontoyiannis首次提交 2023-05-23 · 修订 2023-11-17 · v2arXiv预印本

离散时间序列与神经脉冲序列的时间因果发现检验AI 解读

We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of $(x_n)$ on $(y_n)$ , causally conditioned on a possibly confounding third time series $(z_n)$ . Under natural Markovian modeling assumptions, it is shown that the null hypothesis, corresponding to the absence of temporally causal influence, is equivalent to the…We consider the problem of detecting causal relationships between discrete time series, in the presence of potential confounders. A hypothesis test is introduced for identifying the temporally causal influence of $(x_n)$ on $(y_n)$, causally conditioned on a possibly confounding third time series $(z_n)$. Under natural Markovian modeling assumptions, it is shown that the null hypothesis, corresponding to the absence of temporally causal influence, is equivalent to the…

宏观 · 时间序列因果推断

关联记录被引 0 次 ↗OpenAlex · 核验 2026-09-30

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