eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)

• URL: http://arxiv.org/abs/2303.02833v1
• Date: Mon, 6 Mar 2023 01:59:45 GMT
• Title: eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
• Authors: Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
• Abstract summary: We present a constraint-based CD approach for autocorrelated and non-stationary time series data (eCDANs) eCDANs can detect lagged and contemporaneous causal relationships along with temporal changes. Experiments on synthetic and real-world data show that eCDANs can identify time influence and outperform the baselines.
• Score: 0.3314882635954752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional temporal causal discovery (CD) methods suffer from high dimensionality, fail to identify lagged causal relationships, and often ignore dynamics in relations. In this study, we present a novel constraint-based CD approach for autocorrelated and non-stationary time series data (eCDANs) capable of detecting lagged and contemporaneous causal relationships along with temporal changes. eCDANs addresses high dimensionality by optimizing the conditioning sets while conducting conditional independence (CI) tests and identifies the changes in causal relations by introducing a surrogate variable to represent time dependency. Experiments on synthetic and real-world data show that eCDANs can identify time influence and outperform the baselines.

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