UCSD Researchers Propose a General Variational Inference-based Framework (MCD) to Infer the Underlying Causal Models as well as the Mixing Probability of Each Sample
Researchers are struggling with the challenge of causal discovery in heterogeneous time-series data, where a single causal model cannot capture diverse causal mechanisms. Traditional methods for causal discovery from time-series data, based on structural causal […]
