  The discussion on causality in human history dates back to ancient Greece,
yet to this day, there is still no consensus. Fundamentally, this stems from
the nature of human cognition, as understanding causality requires abstract
tools to transcend the limitations of human cognition. In recent decades, the
rapid development of mathematical and computational tools has provided new
theoretical and technical means for exploring causality, creating more avenues
for investigation.
  Based on this, this paper introduces a new definition of causality using
category theory, proposed by Samuel Eilenberg and Saunders Mac Lane in 1945 to
avoid the self-referential contradictions in set theory, notably the Russell
paradox. Within this framework, the feasibility of indicator synthesis in
causal inference is demonstrated. Due to the limitations in the development of
category theory-related technical tools, this paper adopts the widely-used
probabilistic causal graph tool proposed by Judea Pearl in 1995 to study the
application of causal inference in personal credit risk management. The
specific work includes: research on the construction method of causal inference
index system, definition of causality and feasibility proof of indicator
synthesis causal inference within this framework, application methods of causal
graph model and intervention alternative criteria in personal credit risk
management, and so on.
