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.