 But this package also implies more. Turn to the causal arrow. As a very rough first approximation, causation can be analyzed probabilistically. Cause \(C\) causes effect \(E\) just in case \(C\) is prior to \(E\) and the probability of \(E\) given \(C\) and background \(B\) is greater than the probability of \(E\) given \(B\) alone. Of course, there are major problems with this account (see the entry on Probabilistic Causation). Yet the core intuition appears to come from the package, as one gets the temporal priority of causes from the Past Hypothesis and the probabilities from statistical mechanics. Together, they are claimed to explain why we can manipulate causes to produce effects but not vice versa. Turn to the epistemic arrow. Reflect on the nature of records. When you weigh yourself on a scale, one produces a record of one’s weight. This record is based on an inference comparing the states of the scale at two different times. I’m (say) 180 lbs if the scale was in its functioning ready state at 0 lbs before I stepped on it. The idea, very loosely (see Albert 2000, 2015 and Loewer 2012 for the details) is that the Past Hypothesis effectively is the world’s Ready State. This highly constrained state is what causes there to be macroscopic traces of the past in the present but not macroscopic traces of the future in the present.