Beyond Correlation and Causation: How Misuse of a Statistical Maxim Hindered Research on Severe Adverse Effects of COVID-19 Vaccines

While the methodological principle “correlation does not imply causation” serves as a crucial safeguard in scientific inquiry, its overly restrictive application can impede legitimate investigative pathways and hinder the proper evaluation of emerging evidence. This phenomenon has been clearly observable in the COVID-19 vaccine safety discussions, where serious adverse events with temporal proximity to vaccination have been frequently rejected without comprehensive examination. This work invites reflection on how the inappropriate application of this methodological principle can obstruct scientific inquiry into adverse event surveillance, potentially affecting scientific credibility. This work applies Kuhnian paradigm theory to analyze how institutionalized frameworks operate simultaneously as mechanisms for knowledge advancement and as barriers to conceptual innovation, particularly in politically contested domains such as vaccine safety evaluation. Additionally, a case report is analyzed using Hill´s criteria for causality, in which an immunohistochemical method was used for the first time to evaluate whether mRNA-based vaccines against SARS-CoV-2 contributed to the cause of death. Results showed that three of the nine criteria demonstrated strong evidence of causality, four were partially fulfilled, and two were not fulfilled. Overall, the report provides approximately 55% causal evidence. This suggests, but does not prove, a causal relationship between the BNT162b2 vaccine and multifocal necrotizing encephalitis. The absence of the nucleocapsid protein does not provide definitive evidence to demonstrate a vaccine origin. An analytical method is described that can distinguish between the wild-type SARS-CoV-2 spike protein and the recombinant spike protein expressed following mRNA vaccination.