Specifically, we develop a probabilistic decomposed slab-and-spike (DSS) model to perform the inference by applying a pair of decomposed spike-and-slab variables for the model coefficients, where the first variable is used to estimate the causal relationship and the second one captures the lag information among different temporal variables. ![]() In this letter, we propose to learn the causal relations as well as the lag among different time series simultaneously from data. However, in many real-world applications, this parameter may vary among different time series, and it is hard to be predefined with a fixed value. To model this process, existing approaches commonly adopt a prefixed time window to define the lag. That is, past evidence would take some time to cause a future effect instead of an immediate response. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. ![]() Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables.
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