ArXiv

Causal Discovery on Irregular Time Series

2026-07-20 · 17:57 UTC ·Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono... ·1 min read
Authors
Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono...
Categories
cs.LG, stat.ME
arXiv
https://arxiv.org/abs/2607.18226v1
PDF
https://arxiv.org/pdf/2607.18226v1

Brief

Causal discovery on irregular time series: the authors extend PCMCI+ to handle irregularly sampled multivariate event streams by aggregating causal influence over predefined temporal windows rather than fixed lags. Evaluated on synthetic irregular event streams across varying signal-to-noise ratios, the approach consistently recovers ground-truth graphs and substantially outperforms standard PCMCI+.

Why it matters

Extends PCMCI+ to irregularly sampled multivariate time series by aggregating causal influence over predefined temporal windows instead of fixed discrete lags (Martim Penim et al., arXiv:2607.18226v1, published 2026-07-20).

Key details

  • On synthetic irregular event streams evaluated across different signal-to-noise ratios, the method consistently recovers the underlying causal graph and substantially outperforms standard PCMCI+ on irregularly sampled data.
Source evidence

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.