  The traditional i.i.d.-based learning paradigm faces inherent challenges in
addressing causal relationships, which has become increasingly evident with the
rise of applications in causal representation learning.
  Our understanding of causality naturally requires a perspective as the
creator rather than observer, as the ``what...if'' questions only hold within
the possible world we conceive. The traditional perspective limits capturing
dynamic causal outcomes and leads to compensatory efforts such as the reliance
on 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 a
practical implementation, supported by experiments that validate its efficacy.
