Causal Discovery on Irregular Time Series

• URL: http://arxiv.org/abs/2607.18226v1
• Date: Mon, 20 Jul 2026 17:57:45 GMT
• Title: Causal Discovery on Irregular Time Series
• Abstract summary: Causal discovery methods have shown strong performance in temporal systems.<n>Many real-world tasks require dealing with irregularly sampled streams of events.<n>We propose an extension of PCMCI+ to allow for handling irregular time series.
• Score: 6.797750633873643
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Causal discovery methods have shown strong performance in temporal systems, but they typically rely on regular and discrete lag structures, limiting their applicability to regularly sampled data. However, many real-world tasks require dealing with irregularly sampled streams of events, such as sensor streams, healthcare data, and financial transactions. In this work, we propose an extension of PCMCI+, a state-of-the-art method for causal discovery on regular multivariate time series, to allow for handling irregular time series. Instead of modelling causal relations through fixed-lag dependencies, our method aggregates causal influence over predefined temporal windows. We evaluate our method on synthetic irregular event streams with known causal structures under different signal-to-noise ratios, showing that it consistently recovers the underlying causal graph and substantially outperforms the standard PCMCI+ on irregularly sampled data.

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