Deciding on a Measure of Effect under Indeterminism ()
ABSTRACT
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.
Share and Cite:
Shahar, D. (2016) Deciding on a Measure of Effect under Indeterminism.
Open Journal of Epidemiology,
6, 198-232. doi:
10.4236/ojepi.2016.64022.