PCMCI+

PCMCI+ extended for causal discovery on irregular time series

A new arXiv preprint proposes an extension of PCMCI+, a leading causal discovery method for multivariate time series, to handle irregularly sampled data by aggregating causal influence over temporal windows rather than fixed lags. The method is evaluated on synthetic irregular event streams with known causal structures across varying signal-to-noise ratios, consistently recovering the underlying causal graph and outperforming standard PCMCI+. This addresses a practical gap for domains like healthcare, sensor networks, and financial transactions where regular sampling is not guaranteed.