A Data-Driven Two-Phase Multi-Split Causal Ensemble Model for Time Series

arXiv:2403.04793v1 Announce Type: new
Abstract: Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to identify linear relationships, while others are applicable for non-linearities. Algorithms further vary in their sensitivity to noise and their ability to infer causal information from coupled vs. non-coupled time series. Therefore, different algorithms often generate different causal relationships for the …

abstract algorithms arxiv causal inference cs.ai cs.lg data data-driven dataset ensemble however identify inference instance linear relationships research series stat.me time series type