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The traditional i.i.d.-based learning paradigm faces inherent challenges inaddressing causal relationships, which has become increasingly evident with therise of applications in causal representation learning.  Our understanding of causality naturally requires a perspective as thecreator rather than observer, as the ``what...if'' questions only hold withinthe possible world we conceive. The traditional perspective limits capturingdynamic causal outcomes and leads to compensatory efforts such as the relianceon hidden confounders. This paper lays the groundwork for the new perspective,which enables the \emph{relation-first} modeling paradigm for causality. Also,it introduces the Relation-Indexed Representation Learning (RIRL) as apractical implementation, supported by experiments that validate its efficacy.

Relation-First Modeling Paradigm for Causal Representation Learning toward the Development of AGI