🤖 AI Summary
This study addresses the limitations of existing causal discovery methods, which rely on regular sampling and fixed lag structures and thus struggle with irregularly sampled time series commonly found in sensor, medical, and financial domains. To overcome this challenge, the work extends the PCMCI+ framework—originally designed for regularly spaced time series based on conditional independence tests—to irregular event streams by introducing a time-window aggregation mechanism. This mechanism replaces conventional fixed-lag modeling and enables time-aware causal inference. Empirical evaluations demonstrate that the proposed approach accurately recovers ground-truth causal graphs across synthetic irregular datasets under varying signal-to-noise ratios, significantly outperforming the original PCMCI+ and effectively eliminating the dependency on regular temporal structure.
📝 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.