EmpiriForge: A Bilingual AI Agent Toolkit for Empirical Research

EmpiriForge is an open-source project focused on providing bilingual AI agent capabilities for economics and social science research. It supports causal inference, reproducible writing, and scientific workflow automation, helping researchers efficiently complete the entire process from data exploration to paper writing.

Empirical research faces challenges such as tedious data processing, difficulty in selecting statistical methods, insufficient result reproducibility, and barriers to cross-language academic writing. EmpiriForge is positioned as an intelligent assistant for researchers, supporting both Chinese and English bilingual environments through modular skill design to facilitate international collaborative research.

EmpiriForge includes four core functional modules:

1. Causal Inference Support: Built-in guides for multiple methods such as instrumental variable method and difference-in-differences method;
2. Reproducible Economics Writing: Structured templates and automated document generation ensure the research process can be recorded and verified;
3. Scientific Workflow Automation: Intelligent workflows from data cleaning to report generation;
4. Bilingual Support System: Accurately converts Chinese and English academic expressions, adapting to academic norms and styles.

EmpiriForge's application scenarios include:

• A learning tool for economics graduate students and young scholars, lowering the threshold for learning empirical research;
• Helping senior researchers improve efficiency and reduce repetitive work;
• Standardizing workflows in team research to ensure consistency and reduce communication costs.

EmpiriForge adopts a modular design, with independent and combinable skill units, offering high extensibility. It is compatible with Python (pandas, statsmodels) and R language statistical packages, allowing users to work in their familiar environments.

As an open-source project, EmpiriForge welcomes community contributions of research templates, code snippets, etc. It is expected to become an important infrastructure in the field of empirical research in the future, assisting researchers to focus on creative and critical work.