Abstract

Modern biomedical research relies heavily on data-driven decision-making, elevating the distinction between statistical correlation and true causality to a foundational challenge in biostatistics and bioinformatics. In high-dimensional datasets containing millions of variables—such as genetic variants, transcriptomic profiles, clinical phenotypes, and environmental exposures—identifying meaningful relationships is highly complex. Emerging researchers frequently conflate predictive correlations with causal mechanisms. Misinterpreting these statistical associations can lead to erroneous biological conclusions, ineffective therapeutic interventions, and flawed clinical recommendations. To address this, this paper clarifies the conceptual and mathematical boundaries between correlation and causality within biomedical frameworks, evaluating the utility of Fuzzy Cognitive Maps (FCMs) as an interpretable methodology for structuring and communicating causal hypotheses. As semi-quantitative networks, FCMs model biological components as nodes and their directed interactions as weighted edges, successfully capturing the direction, sign, and approximate magnitude of hypothesized influences. This approach effectively accommodates feedback loops and integrates expert knowledge with empirical data, bridging the gap between purely statistical associations and mechanistic biological reasoning. However, while FCMs serve as powerful tools for hypothesis generation and system visualization, they do not replace formal structural causal inference methodologies. Ultimately, an FCM function as a formal representation of a hypothesized causal network rather than an autonomous mathematical mechanism to prove causality from observational data alone.

Keywords:Fuzzy cognitive maps; Causality; Correlation; Biostatistics; Bioinformatics; Systems biology; Causal modeling; Biological networks