On occasion of the MCMP workshop on mathematical philosophy (13 & 14 Sep 2011) as part of the DGPhil conference 2011, Roland Poellinger gave a short presentation titled "Disentangling Nets for Causal Inference". The topics: What problems arise, when Bayes net causal models are augmented by the insertion of non-causal, non-directional knowledge? How can structural knowledge about given situations be enriched to decide about the utility of available semi-DAGs? And is there any possibility to regain the Markov condition for consistent computation of causal claims from graphs that unify directional and undirectional information? Watch the talk on iTunes U!