TITLE:
Deciding on a Measure of Effect under Indeterminism
AUTHORS:
Doron J. Shahar
KEYWORDS:
Measure of Effect, Measure of Frequency, Indeterminism, Causation, Causal Diagram
JOURNAL NAME:
Open Journal of Epidemiology,
Vol.6 No.4,
November
9,
2016
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.