Continued...

In the cause of observational causality, another important aspect is that the cause should predict the effect better than the effect predicts itself. In this case we indeed find that Excess Mortality predicts itself poorly —of course, for a time delay equal to 0, it does predict itself, for all other time delays, it has little to no explanatory power.

From this we can conclude that, based on the data sets used, Administered Doses do cause Excess Mortality. Of course, our “briliant experts” are still stuck in the 19th century wrt statistical methods. I do not expect that they will be able to explain the excess mortality with their current methods, simply because 5 months is a hugh time delay, and because here is typically little knowledge of observational causal methods.

About the method The method used is based on correlations. The current state-of-the-art is based on Information Theory, but that’s too out of the ordinary for layman, and even experts, so we better not go there. When I have time in a couple of weeks I might run the information theoretical causal analytics.

About the data As mentioned, the data sets from Holden’s sources were used. We selected only EU countries, we did not differentiate between sexes and age groups. Finally we used monthly data due to time restrictions on my side (I simply do not have time to search for, or create weekly data sets for the excess mortality)."