Causal Representation Learning with Quantum Encodings for Personalized Decision Policies in Adaptive Systems

Personalized adaptive systems frequently learn from biased logs generated by historical policies, where spurious correlations and environment shift degrade both generalization and cohort safety. This paper introduces a unified pipeline that couples causal representation learning with quantum encodings to produce stable embeddings for offline-trained decision policies. Across environment-split evaluations, the proposed quantum causal representation reduced the invariance gap from 0.118 (baseline classical) and 0.067 (causal classical) to 0.041, while improving reward prediction AUROC from 0.791 and 0.816 to 0.842. Under drifted splits, representation shift-drop decreased from 7.6% to 2.5%, indicating stronger transfer under distributional change. Policies trained on these embeddings achieved higher normalized off-policy value, improving from 0.456 (baseline) and 0.472 (causal classical) to 0.489, while reducing shift gap from 6.9% to 2.6% and increasing worst-cohort uplift to 0.028. Drift stress tests showed sustained value retention at severe drift (level 3), with values 0.401 (baseline), 0.433 (causal classical), and 0.459 (quantum causal), accompanied by lower calibration error (0.084, 0.068, and 0.056, respectively). Ablations demonstrated a capacity sweet spot at 6 qubits and 4 layers, delivering strong value (0.489) at deployable latency (6.7 ms), whereas higher capacity yielded marginal value gains (0.491) at increased latency (9.4 ms). These results indicate that integrating causal invariance with carefully budgeted quantum feature maps improves personalization effectiveness, robustness, and reliability in adaptive decision-making from logs.