 Some of these measures can be formulated probabilistically and have been suggested as measures of causal strength and outcome measures for clinical trials (Edwards 1963; Eells 1991; Fitelson &amp; Hitchcock 2011; Greenland 1987; Peirce 1884; Sprenger 2018; Sprenger &amp; Stegenga 2017). For example, \(\pi_{D} = p(\r{S}\mid \r{T}) - p(\r{S}\mid \neg \r{T})\) represents the difference and \(\pi_R = p(\r{S}\mid \r{T}) / p(\r{S}\mid \neg \r{T})\) the ratio of success rates in treatment and control conditions. \(\pi_W\) can be interpreted as the prognostic weight of evidence that treatment provides for success (i.e., as the log-Bayes factor), \(\pi_{Y}\) is Yule’s (1903) measure of association, \(\pi_{O}\) is the log-odds ratio familiar from epidemiological data analysis, and \(\pi_C\) I.J. Good’s (1960) measure of causal strength.