 Let \(\bV\) be a set of time-indexed, binary variables, which we assume to include any common causes of variables in \(\bV\) (so that the correct causal graph on \(\bV\) is a DAG). Let \(*\) be an assignment function that assigns to each variable \(X\) in \(\bV\) one of its possible values. Intuitively, \(*\) identifies the actual value of each variable. We will denote \(*(X)\) by \(x^*\), and \(x'\) will denote the non-actual value of \(X\). If \(\bX\) is a set of variables in \(\bV\), \(\bX\) = \(\bx^*\) will be a proposition stating that each variable in \(\bX\) takes the actual value assigned by \(*\). Let P be a probability function on \(\bV\) representing objective probability, which we assume to satisfy the Markov and Minimality Conditions (Sections  3.3  and  3.4  above). We also assume that P assigns positive probability to every possible assignment of values to variables in \(\bV\).