A novel machine learning model called Temporal Autoencoders for Causal Inference (TACI) accurately detects changing cause-and-effect relationships in complex, time-varying systems like weather patterns and brain activity. By analyzing both synthetic and real data, TACI captures dynamic interactions and quantifies shifts in strength or direction over time. Tested on long-term weather data and brain imaging in monkeys, TACI successfully pinpointed when causal connections emerged, weakened, or reversed.

Menopausal hormone therapy (MHT) has nuanced effects on brain health, influenced by factors like age, treatment duration, and past surgical history. The study found that current MHT users had higher brain age gaps and smaller hippocampal volumes, while past users showed no significant differences compared to non-users.