Working on my understanding of the elpd-loo estimate provided by loo/kfold and came across this paper on information crtieria http://www.stat.columbia.edu/~gelman/research/unpublished/waic_understand.pdf. I’m specifically interested in the section on statistical and practical significance. I have copied the most relevant section below:

For example, consider two models for a survey of n voters in an American election, with one model being completely empty (predicting p = 0.5 for each voter to support either party) and the other correctly assigning probabilities of 0.4 and 0.6 (one way or another) to the voters. Setting aside uncertainties involved in fitting, the expected log predictive probability is log(0.5) =−0.693 per respondent for the first model and 0.6 log(0.6)+0.4 log(0.4) =−0. 673 per respondent for the second model…

If I have a continuous response variable, measured across 30 participants the elpd_loo is the average total elpd for each participant across each simulation correct? So to get the average elpd for an individual I would divide by the number of 30, so say that leaves me with a per participant difference in elpd of 1.5 between two models. Is it possible to express this within terms of the original scale? If I just take exp(1.5) = 4.48 is that at all an estimate of the difference in estimated predictive error between the two models in terms of the original scale?