A simple observational causal analysis of August 9 came to the same conclusion. This was based on European data.
______________
Edit, as one has to be a member to read the content of he …
A simple observational causal analysis of August 9 came to the same conclusion. This was based on European data.
______________
Edit, as one has to be a member to read the content of he linke above, I took the liberty to post the text here.
"This is my promised reaction to Holden. Let’s start with the conclusion based on the EU data from the data sets that Holden used (see picture).
<1436-ExcessMortality.png>
What the figure shows is the “correlation coefficient” as function of a temporal delay, that is, time shift. A negative time shift indicates that excess mortality is preceding the vaccination status.
A positive time shift indicates that the administered doses precedes the excess mortality.
The optimal time shift is defined as that time shift that maximizes the absolute pearson coefficient. We search for the highest, or the lowest value. The lowest value is around -0.3, while the highest value is around 0.8. Because the absolute value of the highest,+0.8, is larger than the absolute value of the lowest, +0.3, the optimal time shift is + 5 months. That is, Administered Doses precedes Excess mortality.
As Holden indeed mentioned, we do find a negative relation also when there is no shift. However, the explanatory power of +0.8 is way larger than -0.3. The reason why I used “causes” instead of causes is that there are several characteristics of causal relations. What we tested now is “temporal precedence”.
Dr. John Campbell, who used to be skeptical of the "anti-vaxxers" got this video banned from his YouTube channel because he went over the UK Yellow Card death reports:
Have you considered trying to get the attention of key actuaries with this analysis? I know you said you were more on the tech end but it might be possible to challenge the official narrative fairly effectively if you can get the attention of certain individuals, some of whom would have access to their own corporate data which they could potentially analyze. For instance, there's an upcoming webinar sponsored by the CCA in Nov (https://www.ccactuaries.org/event-detail/2022/11/09/default-calendar/measuring-covid-severity-what-are-the-odds-and-ends-) -- maybe you (or you and Steve) could reach out to these speakers?
Actually, no. The reason is twofold. The method that I developed (not the one used in this post, this is a simple approach that is only applicable in a limited set of cases) is new, and still some research questions need to be answered. As TPTB have a rather ugly track record in silencing even well known, and well connected people, I do not want to run the risk that this new approach will be "killed". However, together with some others I'm investigating the possibility of implementing causal analytics on a server, and make it freely available (for a limited data set, i.e., thousands of samples, and maximal twenty parameters). In this way we will be somewhat "hardened" against the nefarious actors who killed science.
The second reason is also very close to my heart. Years ago I came up with a novel way to generate hydrogen using only warmth. This takes basically most of my time. I'm not even sure if it works, but I will never forgive myself if I won't try to find out.
Don't. The text is rather self-explanatory (I hope). The graph shows an "S" shaped curve. The top of the "S" occurs @ 5 months, meaning that the variation in the # of vaccines administered 5 months ago, are the best "eplanation" for he variations of excess mortality now.
You should interview Dr. Chris Martenson, one of the earlier callers of BS on the C19 events. He runs a YouTube channel called Peak Prosperity. He recently did a wonderful mutual interview with Dr. Bret Weinstein.
I did. Causal analytics is kind of a hobby of mine, which started while working in a High Tech company. I have a couple of papers published. And I'm thinking about a startup using a specific propriatory method used on Information Theory. Still a couple of theoretical problems have to be resolved, but the results thusfar are pretty amazing. For more information, please pop me an email. I'm very hesitant to use my own name, as TPTB have a proven track record of destroying everything of value.
Edit: ahh, I noticed that no private messages can be send. Well, hopefully in a couple of months our free causal analytics service will be available. Your data driven substack will give ample opportunity to post some of it's results.
I remember this post and wonder if it is quasi-prophetic. We are now essentially hitting the 5 month mark on the bivalent jabs from sept and oct 2022. Seems like it is kicking in now.
Holden did a correlation check. He found a negative correlation. However, his methodology was incomplete. I used this approach to show him that one has to apply time shifts. The figure I refer to is the correlation coefficient versus the time delay based on my approach.
For a time delay of zero, the negative correlation was found that Holden referred to. However, the correlation coefficient was maximized for a 5 months delay.
This was merely an exercise to show Holden how to perform this type of analysis and not to jump to hasty conclusions.
I used the second dose. For all doses, a positive correlation coefficient is found for certain time shifts. However, because monthly data was used, the significance of the correlation coefficient for the boosters was not sufficiently high enough. As indicated, I would have preferred weekly data, plus a different method. Unfortunately I do not have a lot of time.
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)."
JP, I think you're referring to the butterfly effect, but that effect is nonsense. A snowball can keep expanding going down a steep wet snowy slope, but once the slope ends, it stops expanding and stops moving. The air movements from butterfly wing motion diminish over time. There's nothing to make them expand. Air molecules don't attach to the small air mass around a butterfly.
Conventional science is plagued with bad assumptions. Quantum physics is in the same boat. See some of the papers in the Quantum Physics section of this site: http://milesmathis.com/
I can mail you the full post. I took the liberty to post the text here. Please indicate if this pollutes the comment setcion in your opinion. if so, I'll delete it.
I am currently kicking myself because I saw Dave's post three weeks ago, and when Steve posted this new substack article, I didn't connect the two. Two different approaches coming up with the same answer...amazing. I don't pretend to understand the stats - I can barely do correlations when python does all the hard work.
Dave also theorized that there could be two peaks; one smaller one immediately following vaccination (visible in VAERS), and another much more impactful one 5 months in (visible in the larger data sets). Maybe - two methods of action at work?
If true, the "second peak" might mean that Steve has ... grossly undercounted the deaths by relying on VAERS numbers, which only looks at that first peak.
https://peakprosperity.com/community/general-discussion-and-questions/administered-doses-cause-excess-mortality-with-a-5-months-delay-repost/#post-188295
A simple observational causal analysis of August 9 came to the same conclusion. This was based on European data.
______________
Edit, as one has to be a member to read the content of he linke above, I took the liberty to post the text here.
"This is my promised reaction to Holden. Let’s start with the conclusion based on the EU data from the data sets that Holden used (see picture).
<1436-ExcessMortality.png>
What the figure shows is the “correlation coefficient” as function of a temporal delay, that is, time shift. A negative time shift indicates that excess mortality is preceding the vaccination status.
A positive time shift indicates that the administered doses precedes the excess mortality.
The optimal time shift is defined as that time shift that maximizes the absolute pearson coefficient. We search for the highest, or the lowest value. The lowest value is around -0.3, while the highest value is around 0.8. Because the absolute value of the highest,+0.8, is larger than the absolute value of the lowest, +0.3, the optimal time shift is + 5 months. That is, Administered Doses precedes Excess mortality.
As Holden indeed mentioned, we do find a negative relation also when there is no shift. However, the explanatory power of +0.8 is way larger than -0.3. The reason why I used “causes” instead of causes is that there are several characteristics of causal relations. What we tested now is “temporal precedence”.
Continued in Reply,,,
You can also see these effects in CDC's provisional death data - filed under "unknown causes"...here's the first quickie analysis I did back in January - https://wholistic.substack.com/p/cdc-data-supports-mysterious-40-increase
And here's an update I did on August 25, and the data is even worse, but the media is blaming the deaths on everything but the vaccines: https://wholistic.substack.com/p/excess-deaths-lets-blame-everything
Dr. John Campbell, who used to be skeptical of the "anti-vaxxers" got this video banned from his YouTube channel because he went over the UK Yellow Card death reports:
https://wholistic.substack.com/p/the-censored-john-campbell-video
But followed up with this one showing all the excess data without mentioning the vaccines per se:
https://wholistic.substack.com/p/dr-john-campbells-second-video-on
Have you considered trying to get the attention of key actuaries with this analysis? I know you said you were more on the tech end but it might be possible to challenge the official narrative fairly effectively if you can get the attention of certain individuals, some of whom would have access to their own corporate data which they could potentially analyze. For instance, there's an upcoming webinar sponsored by the CCA in Nov (https://www.ccactuaries.org/event-detail/2022/11/09/default-calendar/measuring-covid-severity-what-are-the-odds-and-ends-) -- maybe you (or you and Steve) could reach out to these speakers?
Actually, no. The reason is twofold. The method that I developed (not the one used in this post, this is a simple approach that is only applicable in a limited set of cases) is new, and still some research questions need to be answered. As TPTB have a rather ugly track record in silencing even well known, and well connected people, I do not want to run the risk that this new approach will be "killed". However, together with some others I'm investigating the possibility of implementing causal analytics on a server, and make it freely available (for a limited data set, i.e., thousands of samples, and maximal twenty parameters). In this way we will be somewhat "hardened" against the nefarious actors who killed science.
The second reason is also very close to my heart. Years ago I came up with a novel way to generate hydrogen using only warmth. This takes basically most of my time. I'm not even sure if it works, but I will never forgive myself if I won't try to find out.
Thank you, Dave. 💡👀
PeakProsperity link is blocked for people without an account. I have 600+ accounts and would rather not get another. :)
Chris Martenson maintains an open YouTube channel, PeakProsperity.
Apart from the YT channel, there is a very active community on the P..p...com.
I am a member.
I am a member too but you still have to pay to read this article
Don't. The text is rather self-explanatory (I hope). The graph shows an "S" shaped curve. The top of the "S" occurs @ 5 months, meaning that the variation in the # of vaccines administered 5 months ago, are the best "eplanation" for he variations of excess mortality now.
Who authored the post on the site? This is awesome. We get the same result using a completely different data set
You should interview Dr. Chris Martenson, one of the earlier callers of BS on the C19 events. He runs a YouTube channel called Peak Prosperity. He recently did a wonderful mutual interview with Dr. Bret Weinstein.
https://www.youtube.com/watch?v=aOT6nzzKrO8
I did. Causal analytics is kind of a hobby of mine, which started while working in a High Tech company. I have a couple of papers published. And I'm thinking about a startup using a specific propriatory method used on Information Theory. Still a couple of theoretical problems have to be resolved, but the results thusfar are pretty amazing. For more information, please pop me an email. I'm very hesitant to use my own name, as TPTB have a proven track record of destroying everything of value.
Edit: ahh, I noticed that no private messages can be send. Well, hopefully in a couple of months our free causal analytics service will be available. Your data driven substack will give ample opportunity to post some of it's results.
Nice work. You referred To a figure from I think it was Holden. Did he find the same 5 month delay?
I remember this post and wonder if it is quasi-prophetic. We are now essentially hitting the 5 month mark on the bivalent jabs from sept and oct 2022. Seems like it is kicking in now.
Holden did a correlation check. He found a negative correlation. However, his methodology was incomplete. I used this approach to show him that one has to apply time shifts. The figure I refer to is the correlation coefficient versus the time delay based on my approach.
For a time delay of zero, the negative correlation was found that Holden referred to. However, the correlation coefficient was maximized for a 5 months delay.
This was merely an exercise to show Holden how to perform this type of analysis and not to jump to hasty conclusions.
All are using the time of the 2nd dose right? So there would be a negative time shift if a person dies after 1st dose, but before 2nd?
I used the second dose. For all doses, a positive correlation coefficient is found for certain time shifts. However, because monthly data was used, the significance of the correlation coefficient for the boosters was not sufficiently high enough. As indicated, I would have preferred weekly data, plus a different method. Unfortunately I do not have a lot of time.
Thank you!
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)."
I came to a similar conclusion of a delay using data in VAERS - https://howbad.info/secondpeak.html
Thank you!
This is great analysis...thank you.
I read your post. If I'm not mistaken, your "optimal" delay is 180 days? If so, again very close to 5 months.
Thank you Craig Paardekooper for all your amazing work.
Some amazing minds on here seeking the truth
That there is so much for us left to learn is a beautiful thing indeed.
JP, I think you're referring to the butterfly effect, but that effect is nonsense. A snowball can keep expanding going down a steep wet snowy slope, but once the slope ends, it stops expanding and stops moving. The air movements from butterfly wing motion diminish over time. There's nothing to make them expand. Air molecules don't attach to the small air mass around a butterfly.
Conventional science is plagued with bad assumptions. Quantum physics is in the same boat. See some of the papers in the Quantum Physics section of this site: http://milesmathis.com/
you must be a member to view your link
I can mail you the full post. I took the liberty to post the text here. Please indicate if this pollutes the comment setcion in your opinion. if so, I'll delete it.
Don’t delete. Great stuff
I am currently kicking myself because I saw Dave's post three weeks ago, and when Steve posted this new substack article, I didn't connect the two. Two different approaches coming up with the same answer...amazing. I don't pretend to understand the stats - I can barely do correlations when python does all the hard work.
Dave also theorized that there could be two peaks; one smaller one immediately following vaccination (visible in VAERS), and another much more impactful one 5 months in (visible in the larger data sets). Maybe - two methods of action at work?
If true, the "second peak" might mean that Steve has ... grossly undercounted the deaths by relying on VAERS numbers, which only looks at that first peak.
I'm not quite sure what to think about that.
What to think?
Welcome to the shitshow.