  How should social scientists understand and communicate the uncertainty of
statistically estimated causal effects? I propose we utilize the posterior
distribution of a causal effect and present the probability of the effect being
greater (in absolute terms) than different minimum effect sizes. Probability is
an intuitive measure of uncertainty for understanding and communication. In
addition, the proposed approach needs no decision threshold for an uncertainty
measure or an effect size, unlike the conventional approaches. I apply the
proposed approach to a previous social scientific study, showing it enables
richer inference than the significance-vs.-insignificance approach taken by the
original study. The accompanying R package makes my approach easy to implement.
