 That is, buying the property should not change our rational degree of belief in who wins the election. Pearl (2016) considers this response an “overkill” and notes that probabilistic associations are not a good means of expressing causal tendencies. Therefore he proposes a causal sure-thing principle that we have encountered in  Section 3.4:  If one is considering two acts \(f\) and \(g\), and the probability distribution of \(\r{B}\) does not change depending on whether one intervenes to choose \(f\) or \(g\), then if one prefers \(f\) to \(g\) whether or not \(B\) occurs, one prefers \(f\) unconditionally. The italicized condition ensures that the partitioning variable is not an effect of the intervention, and thus rules out Simpson’s reversals (see  section 3.4).  Note that Pearl’s formulation, but not Jeffrey’s, allows to apply the (causal) sure-thing principle to observational data, where states and acts may be statistically dependent without indicating genuine causation (e.g., because of self-selection effects).