Event Detail

Although a great deal of progress has been made in the last two decades on formally representing causal systems, axiomatizing the relations between causation and probabilistic independence, and on developing algorithms that learn about causal structure from data, there are still many serious conceptual problems with what are now often referred to as graphical causal models. In this talk I will briefly review the progress that has been made, but then focus on the challenges that remain unsolved.