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\section{Conclusion}\label{sec:conclusion}
In this paper, we introduced the \emph{relativity of causal knowledge}.
We established the formal definition of \emph{relative causal knowledge} (RCK) via a category-theoretic treatment for SCMs, their interventions, and related $\boldsymbol{\alpha}$-abstractions.
Within the relativity of the causal knowledge paradigm, SCMs are inherently imperfect, subjective representations embedded within networks of relationships.  
By encoding causal knowledge in convex spaces of probability measures and constructing network sheaves and cosheaves, we enabled the transfer of causal knowledge in an interventionally consistent manner while considering diverse perspectives across the network.
Our work opens exciting research avenues in cohomology, Hodge(-like), and learning theory for RCK, paving the way for context-aware, robust AI systems.