Estimating causal effects is a principal goal in epidemiology and other branches of science. Nonetheless, what constitutes an effect and which measure of effect is pre-ferred are unsettled questions. I argue that, under indeterminism, an effect is a change in the tendency of the outcome variable to take each of its values, and then present a critical analysis of commonly used measures of effect and the measures of frequency from which they are calculated. I conclude that all causal effects should be quantified using a unifying measure of effect called the log likelihood ratio (which is the log probability ratio when the outcome is a discrete variable). Furthermore, I suggest that effects should be estimated for all causal contrasts of the causal variable (i.e., expo-sure), on all values of the outcome variable, and for all time intervals between the cause and the outcome. This goal should be kept in mind in practical approximations.