Prompt-Structured Priors for Causal Graph Modeling in Career Growth Path Planning: A Reproducible Simulation Benchmark with Public-Data Anchoring

Career growth path planning is still dominated by statistical association models that summarize historical transitions but do not explicitly represent the causal mechanisms linking capability development, project exposure, policy support, performance improvement, and promotion outcomes. This study develops a reproducible simulation benchmark for evaluating whether prompt-structured priors, when coupled with dual validation, can help assemble intervention-ready career causal graphs. A structural causal model (SCM) first generated 20,000 synthetic career trajectories with known ground-truth dependencies among ten variables, including education, experience, training hours, certification, project exposure, performance, and promotion. Four prompt families-zero-shot, few-shot, Chain-of-Thought (CoT), and CoT plus schema constraints-were instantiated through a controlled prompt-response emulator so that prompt structure could be studied independently of vendor-specific model drift. The emulator gradients should therefore be read as literature-informed design assumptions about structured prompting rather than as empirical measurements from any named production LLM. Candidate edges were subsequently refined by data validation and expert-proxy domain rules. In the main 30-run benchmark, the best prompt-only setting (CoT plus schema) achieved an F1-score of 0.842, while the proposed hybrid method achieved an F1-score of 0.959 and an intervention-effect mean absolute error of 0.0046. Run-wise confidence intervals and approximate significance checks further indicated that the hybrid workflow materially outperformed the prompt-only variants under the benchmark protocol. A public employee-promotion dataset (N= 54,808) was further used as an external plausibility anchor, where KPI attainment, awards, previous ratings, training score, and length of service were all positively associated with promotion. The results indicate that prompt-structured priors can be useful as a transparent proposal-and-validation mechanism, but not as a substitute for direct validation on real LLMs, matched comparisons with standard causal-discovery baselines, or real HR deployment settings. Accordingly, the central aim is a domain-specific methodological benchmark for testing prompt-structured proposal mechanisms in career-growth causal modeling, rather than a claim of standalone LLM causal discovery or a universal benchmark for every causal-discovery setting.