June 6, 2024, 4:43 a.m. | Josuan Calderon, Gordon J. Berman

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03212v1 Announce Type: new
Abstract: Most approaches for assessing causality in complex dynamical systems fail when the interactions between variables are inherently non-linear and non-stationary. Here we introduce Temporal Autoencoders for Causal Inference (TACI), a methodology that combines a new surrogate data metric for assessing causal interactions with a novel two-headed machine learning architecture to identify and measure the direction and strength of time-varying causal interactions. Through tests on both synthetic and real-world datasets, we demonstrate TACI's ability to accurately …

abstract arxiv autoencoders causal causal inference causality convolutional cs.lg data fail inference interactions linear methodology non-linear q-bio.qm systems temporal type variables

Senior Data Engineer

@ Displate | Warsaw

Automation and AI Strategist (Remote - US)

@ MSD | USA - New Jersey - Rahway

Assistant Manager - Prognostics Development

@ Bosch Group | Bengaluru, India

Analytics Engineer - Data Solutions

@ MSD | IND - Maharashtra - Pune (Wework)

Jr. Data Engineer (temporary)

@ MSD | COL - Cundinamarca - Bogotá (Colpatria)

Senior Data Engineer

@ KION Group | Atlanta, GA, United States