# Automating psychological hypothesis generation with AI: when large language models meet causal graph

arXiv: https://arxiv.org/abs/2402.14424 · HTML: https://mlsanity.com/paper/2402.14424/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology](https://mlsanity.com/paper/2308.04709/) (2023)
- [Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics](https://mlsanity.com/paper/1906.06843/) (2019)
- [Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency](https://mlsanity.com/paper/2304.11079/) (2023)

## Built on by (most cited first, 15 of 15)

- [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025)
- [34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery](https://mlsanity.com/paper/2505.03049/) (2025)
- [A Survey on Enhancing Causal Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2503.09326/) (2025)
- [LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery](https://mlsanity.com/paper/2506.00844/) (2025)
- [Deep Associations, High Creativity: A Simple yet Effective Metric for Evaluating Large Language Models](https://mlsanity.com/paper/2510.12110/) (2025)
- [Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs](https://mlsanity.com/paper/2502.06075/) (2025)
- ["The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation](https://mlsanity.com/paper/2503.16791/) (2025)
- [Simulating Tabular Datasets through LLMs to Rapidly Explore Hypotheses about Real-World Entities](https://mlsanity.com/paper/2411.18071/) (2024)
- [EvidenceBench: A Benchmark for Extracting Evidence from Biomedical Papers](https://mlsanity.com/paper/2504.18736/) (2025)
- [COGTRL: Training LLMs for Scientific Discovery Assistance using Cognitive Traces via Reinforcement Learning](https://mlsanity.com/paper/2608.30109/) (2026)
- [InterFeat: A Pipeline for Finding Interesting Scientific Features](https://mlsanity.com/paper/2505.13534/) (2025)
- [Student Development Agent: Risk-free Simulation for Evaluating AIED Innovations](https://mlsanity.com/paper/2510.09183/) (2025)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)
- [A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data](https://mlsanity.com/paper/2604.16982/) (2026)
- [HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge](https://mlsanity.com/paper/2609.12260/) (2026)

## Abstract (arXiv)

Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on `well-being', then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p=0.007 and t(59) = 4.32, p<0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.