Bootstrap aggregation and confidence measures to improve time series causal discovery

arXiv:2306.08946v2 Announce Type: replace-cross
Abstract: Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as in Earth sciences, biology, or engineering, to name a few. Recent developments for this causal discovery learning task have shown considerable skill, notably the specific time-series adaptations of the popular conditional independence-based learning framework. However, uncertainty estimation is challenging for conditional independence-based methods. Here, we introduce a novel bootstrap approach designed for time series …

abstract aggregation application arxiv biology bootstrap challenge confidence discovery domains earth earth sciences engineering graphs multivariate series stat.me stat.ml systems time series type