  In Reinforcement Learning-based Recommender Systems (RLRS), the complexity
and dynamism of user interactions often result in high-dimensional and noisy
state spaces, making it challenging to discern which aspects of the state are
truly influential in driving the decision-making process. This issue is
exacerbated by the evolving nature of user preferences and behaviors, requiring
the recommender system to adaptively focus on the most relevant information for
decision-making while preserving generaliability. To tackle this problem, we
introduce an innovative causal approach for decomposing the state and
extracting \textbf{C}ausal-\textbf{I}n\textbf{D}ispensable \textbf{S}tate
Representations (CIDS) in RLRS. Our method concentrates on identifying the
\textbf{D}irectly \textbf{A}ction-\textbf{I}nfluenced \textbf{S}tate Variables
(DAIS) and \textbf{A}ction-\textbf{I}nfluence \textbf{A}ncestors (AIA), which
are essential for making effective recommendations. By leveraging conditional
mutual information, we develop a framework that not only discerns the causal
relationships within the generative process but also isolates critical state
variables from the typically dense and high-dimensional state representations.
We provide theoretical evidence for the identifiability of these variables.
Then, by making use of the identified causal relationship, we construct
causal-indispensable state representations, enabling the training of policies
over a more advantageous subset of the agent's state space. We demonstrate the
efficacy of our approach through extensive experiments, showcasing our method
outperforms state-of-the-art methods.
