Learning Multiscale Non-stationary Causal Structures

• URL: http://arxiv.org/abs/2208.14989v2
• Date: Fri, 17 Nov 2023 21:34:37 GMT
• Title: Learning Multiscale Non-stationary Causal Structures
• Authors: Gabriele D'Acunto, Gianmarco De Francisci Morales, Paolo Bajardi and Francesco Bonchi
• Abstract summary: We introduce the multiscale non-stationary directed acyclic graph (MN-DAG), a framework for modeling multivariate time series data. We devise a method named Multiscale Non-stationary Causal Learner Structure (MN-CASTLE) that uses variational inference to estimate MN-DAGs. We show the superior performance of MN-CASTLE on synthetic data with different multiscale and non-stationary properties compared to baseline models.
• Score: 10.821465726323712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses a gap in the current state of the art by providing a solution for modeling causal relationships that evolve over time and occur at different time scales. Specifically, we introduce the multiscale non-stationary directed acyclic graph (MN-DAG), a framework for modeling multivariate time series data. Our contribution is twofold. Firstly, we expose a probabilistic generative model by leveraging results from spectral and causality theories. Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph. Secondly, we devise a Bayesian method named Multiscale Non-stationary Causal Structure Learner (MN-CASTLE) that uses stochastic variational inference to estimate MN-DAGs. The method also exploits information from the local partial correlation between time series over different time resolutions. The data generated from an MN-DAG reproduces well-known features of time series in different domains, such as volatility clustering and serial correlation. Additionally, we show the superior performance of MN-CASTLE on synthetic data with different multiscale and non-stationary properties compared to baseline models. Finally, we apply MN-CASTLE to identify the drivers of the natural gas prices in the US market. Causal relationships have strengthened during the COVID-19 outbreak and the Russian invasion of Ukraine, a fact that baseline methods fail to capture. MN-CASTLE identifies the causal impact of critical economic drivers on natural gas prices, such as seasonal factors, economic uncertainty, oil prices, and gas storage deviations.

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