\section{Limitations and responsible scope}\label{sec:limitations}

\paragraph{Predictive is not causal.} The learned kernel is observational. A $\operatorname{do}(a)$ interpretation requires sufficient state, consistency, positivity, and control of time-varying confounding. If hidden variables jointly affect action and event, the planning semantics fail even when prediction is calibrated.

\paragraph{Finite full-history worlds are both useful and pathological.} Exhaustive enumeration gives unusually strong implementation checks. It also creates a rapidly expanding state space in which count gates lose coverage. Learned state abstractions are necessary for scale, but abstraction error then becomes a new assumption rather than disappearing.

\paragraph{Function space versus finite models.} Proposition~\ref{prop:factor} is exact in function space. The Transformer experiment adds representation, approximation, optimization, and calibration error. Our results show a readout effect in one controlled architecture, not universal dominance across model classes.

\paragraph{Reward specification.} The scalar reward is declared, not discovered as a universal good. A practical system needs versioned user preferences, multiple value axes, sensitivity analysis, and the ability to reject a scalarization. The present paper analyzes planning conditional on $r$.

\paragraph{Bounds are conservative.} Theorem~\ref{thm:error} is a uniform worst-case guarantee. It can be vacuous in sparse spaces; the experiment reports that failure rather than replacing maximum error with a weighted average. Tighter occupancy-aware guarantees would require restrictions on policy shift or concentrability.

\paragraph{High-stakes deployment.} Medical, legal, financial, employment, and family decisions require domain expertise and human approval. The formalism can support structured scenario comparison; it does not justify autonomous prescription. Longitudinal personal data also require minimization, granular consent, export, deletion, and security controls outside the scope of this paper.

