 It is a general feature of the SEF approach that the model need not include as variables all of the factors that are relevant to the effect under consideration (and indeed no model never does – there are just too many factors). In the Billy/Suzy model above, for example, there are no variables describing the actual and possible states corresponding to causal intermediaries between Billy’s or Suzy’s throwing (or not throwing) and their respective rocks hitting (or not hitting) the bottle. So what determines which variables should and should not be included in the model in order to uncover the causal relationships between the variables we’re interested in?