Mohammad Fesanghary

Causal-TS enables precise causal discovery in dynamic time series, adapting to structural changes for more reliable insights.

In sparse autoregressive settings, CEDAR uncovers causal relationships with remarkable efficiency, requiring only $O(d^2)$ conditional independence tests after initial screening.

GRACE reduces false positives by 99% while recovering nearly all true causal edges in complex time series data, revolutionizing causal discovery efficiency.