On 13 September, 2012, Roland Poellinger gives a talk at the Fifth Workshop on Principles and Methods of Statistical Inference with Interval Probability (WPMSIIP'12) in Munich, the title: "Superimposing Imprecise Evidence onto Stable Causal Knowledge: Analyzing ‘Prediction’ in the Newcomb Case". The talk sketches a possible extension of the CKP framework and outlines an agenda for introducing interval probabilities into causal reasoning, where specific concepts need to be formalized in an imprecise way – (un)reliability as quality of prediction in the Newcomb case. From the abstract:

In this talk I will prepare the ground for a understanding of causality that enables the causal decision theorist to answer Nozick’s challenge with the solution of one-boxing by drawing on the framework of *causal knowledge patterns*, i.e., Bayes net causal models built upon stable causal relations (cf. Pearl 1995 and 2000/2009) augmented by non-causal knowledge (*epistemic contours*). This rendition allows the careful re-examination of all relevant notions in the original story and facilitates approaching the following questions:

- How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge?
- How can the notion of
*prediction*be analyzed – philosophically and formally? - If all relations given in the model represent stable causal knowledge, how can imprecise evidence be embedded formally? Or in other words: How can the
*unreliable predictor*be modeled without discarding the core structure?

Finally, in what way could *unreliable prediction* be modeled with interval probability, as motivated by considerations in Nozick’s treatise? And what should be the interpretation of such a rendition?