Bayesian Inference of Causal Graphs: Current Status and Future Directions

Explore the latest advancements in Bayesian inference of causal graphs through this insightful 48-minute talk by Professor Mikko Koivisto from the Finnish Center for Artificial Intelligence. Delve into the field of causal discovery, which aims to uncover cause-effect relationships between variables using observational data. Examine the potential of Bayesian methods in quantifying uncertainty across competing causal hypotheses. Gain an understanding of the challenges posed by computational complexity in this domain and learn about ongoing research efforts to address these issues. Critically analyze the assumptions required for efficient Bayesian inference, including the concept of causal sufficiency. Benefit from Professor Koivisto's expertise in algorithms and artificial intelligence as he shares his perspectives on the current state and future directions of Bayesian causal graph inference.

Mikko Koivisto: Bayesian inference of causal graphs: where we are and where we should go