The study introduces a groundbreaking approach to computational hypothesis generation in psychology by combining large language models (LLMs) with causal knowledge graphs. The researchers analyzed 43,312 psychology articles using an LLM to extract causal relation pairs, creating a specialized causal graph. They then applied link prediction algorithms to generate 130 potential psychological hypotheses focusing on "well-being." These hypotheses were compared against those conceived by doctoral scholars and those produced solely by the LLM. The results showed that the combined approach of LLMs and causal graphs mirrored expert-level insights in terms of novelty, significantly outperforming LLM-only hypotheses. Deep semantic analysis further confirmed the algorithm's ability to incorporate profound conceptual insights and a broader semantic spectrum. This work demonstrates the potential of integrating LLMs with machine learning techniques to revolutionize automated discovery in psychology, extracting novel insights from extensive literature.The study introduces a groundbreaking approach to computational hypothesis generation in psychology by combining large language models (LLMs) with causal knowledge graphs. The researchers analyzed 43,312 psychology articles using an LLM to extract causal relation pairs, creating a specialized causal graph. They then applied link prediction algorithms to generate 130 potential psychological hypotheses focusing on "well-being." These hypotheses were compared against those conceived by doctoral scholars and those produced solely by the LLM. The results showed that the combined approach of LLMs and causal graphs mirrored expert-level insights in terms of novelty, significantly outperforming LLM-only hypotheses. Deep semantic analysis further confirmed the algorithm's ability to incorporate profound conceptual insights and a broader semantic spectrum. This work demonstrates the potential of integrating LLMs with machine learning techniques to revolutionize automated discovery in psychology, extracting novel insights from extensive literature.