 Bayesian networks, as employed in Pearl’s account of causation (2000), have been used to model the effects of voter dependencies on the jury theorem and to distinguish between stronger and weaker variants of conditional independence (Dietrich and List 2004; Dietrich and Spiekermann 2013). This work suggests that, under realistic assumptions, Condorcet’s asymptotic conclusion fails to hold, and the probability of a correct majority decision converges, at most, to a number strictly below 1 (e.g., the probability that the jury’s evidence is not misleading). Furthermore, Dietrich (2008) has argued that Condorcet’s original two assumptions are never simultaneously justified, in the sense that, even when they are both true, one cannot obtain evidence to support both at once.