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:

  1. How may causality in general be understood to allow causal inference from hybrid patterns encoding subjective knowledge?
  2. How can the notion of prediction be analyzed – philosophically and formally?
  3. 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?