STATA BRIDGE is not just another automation tool. It is the missing synapse between your brain's causal intuition and Stata's econometric engine. Imagine dictating research questions in plain English and watching Stata execute the perfect regression specification, interpret the output, and even suggest robustness checks—all without touching the command window.
This repository transforms the traditional "reg monkey" workflow into an intelligent dialogue where Large Language Models (LLMs) become your analytical partners, not just code generators.
Most Stata automation tools treat you like a typist. STATA BRIDGE treats you like a scientist. Instead of memorizing syntax or copy-pasting boilerplate code, you describe your research question, and the system:
- Parses your intent using NLP
- Maps it to appropriate Stata commands (regress, ivreg2, probit, etc.)
- Executes the analysis with error handling
- Explains the output in plain language
- Suggests next steps (heteroskedasticity tests, interaction effects, etc.)
This is not syntax assistance. This is analytical partnership.
graph TD
A[User Query in Natural Language] --> B{STATA BRIDGE API Gateway}
B --> C[OpenAI GPT-4 / Claude 3.5]
B --> D[Local LLM (Ollama)]
C --> E[Intent Parsing Engine]
D --> E
E --> F[Stata Command Generator]
F --> G[Stata Execution Sandbox]
G --> H[Output Parser + Error Handler]
H --> I[Interpretation Engine]
I --> J[User-Friendly Response]
J --> K[Suggest Robustness Checks]
K --> A
The system operates as a closed feedback loop. Each analysis improves the next through context-aware learning.
Create a bridge_config.yml file in your project root:
# STATA BRIDGE Configuration Profile
stata_path: /usr/local/stata17/stata-se
log_directory: ./bridge_logs
llm_provider: openai # Options: openai, claude, ollama
model: gpt-4-turbo
temperature: 0.3 # Lower = more deterministic
safety_features:
allow_destructive: false # Prevents drop, replace, etc.
max_obs_warning: 10000 # Warns if dataset exceeds this
output_limit: 5000 # Max lines returned
causal_presets:
default_controls: [age, education, income]
default_fe: true
cluster_se: trueThis profile ensures you never accidentally drop your master dataset or run models on massive files without warning.
# Install the bridge
pip install stata-bridge
# Launch interactive session
stata-bridge --config bridge_config.yml
# Then type:
>> "Run a regression of wages on education, controlling for experience and industry, with state fixed effects"
# System responds:
[Parsing Intent] Detected: OLS regression with FE
[Mapping] Using reghdfe with absorbs(state)
[Execution] reghdfe wage education experience i.industry, absorb(state) vce(cluster state)
[Results] Education coefficient: 0.1243** (p=0.002)
[Interpretation] Each additional year of education is associated with a 12.4% wage increase, controlling for...
[Suggestions] Consider: 1) Heckman selection for labor force participation 2) IV for ability biasNo typing of Stata commands. No memorizing options. Just your research question and intelligent analysis.
| OS | Status | Notes |
|---|---|---|
| macOS 13+ | ✅ Full Support | Native M1/M2/M3 support |
| Windows 10/11 | ✅ Full Support | WSL2 recommended for Unix paths |
| Linux (Ubuntu 20.04+) | ✅ Full Support | Stata 16+ required |
| Windows Server | No GUI features | |
| Old macOS (12-) | ❌ | Unsupported |
| Feature | Description | Status |
|---|---|---|
| Multilingual Queries | Ask in English, Chinese, Spanish, French, Japanese | ✅ v1.0 |
| Causal DAG Detection | Automatically suggests DAG-based identification strategies | ✅ v2.0 |
| Responsive UI | Web interface + CLI + Jupyter notebook extension | ✅ v1.0 |
| 24/7 API Support | Runs as background service with health checks | ✅ v1.2 |
| Sensitivity Analysis | Automatically runs robustness checks | ✅ v2.1 |
| Code Anonymization | Strips identifying info before sharing queries | ✅ v1.3 |
| Batch Processing | Run hundreds of specifications from a single query | ✅ v2.0 |
| Error Recovery | Suggests alternatives when models fail to converge | ✅ v1.0 |
STATA BRIDGE supports multiple LLM backends. Choose based on your needs:
- Best for: General econometric reasoning, complex causal inference
- Strength: Understands nuanced statistical concepts (instrumental variables, difference-in-differences)
- Configuration:
export OPENAI_API_KEY="your_key_here" stata-bridge --provider openai --model gpt-4-turbo
- Best for: Long analysis chains, multi-step workflows
- Strength: 100k token context window—can analyze your entire dataset documentation
- Configuration:
export ANTHROPIC_API_KEY="your_key_here" stata-bridge --provider claude --model claude-3-opus-20240229
- Best for: Privacy-sensitive research, offline work
- Strength: No data leaves your machine
- Configuration:
# Install Ollama, pull a model ollama pull llama3.1:70b stata-bridge --provider ollama --model llama3.1:70b
The system automatically adapts its prompting strategy to each LLM's strengths. GPT-4 receives structured prompts with Stata documentation snippets; Claude receives longer analytical chains; local models get simplified tasks with more guardrails.
- Stata automation with LLM: The first tool to truly understand why you're running a regression, not just how
- Causal inference assistant: Moves beyond correlation mining to suggest identification strategies
- AI-powered econometrics: Combines Stata's statistical rigor with LLM's natural language understanding
- Regression analysis automation: Reduces 30-minute coding sessions to 30-second conversations
- Data science code generator: But with domain-specific knowledge of econometric best practices
- Machine learning for Stata: Not replacing your workflow—augmenting it with intelligent suggestions
STATA BRIDGE is an assistive tool, not a replacement for statistical training. The LLM may:
- Suggest incorrect specifications for certain data structures
- Misinterpret your research design
- Generate syntactically valid but semantically wrong Stata code
Always:
- Review generated code before execution
- Validate results with traditional methods
- Understand the econometric assumptions behind each specification
The developers assume no liability for incorrect analysis, publication retractions, or tenure denials resulting from LLM-generated statistical errors.
This project is licensed under the MIT License - see the LICENSE file for details. You are free to use, modify, and distribute, but attribution is appreciated.
| Quarter | Feature |
|---|---|
| Q1 2026 | Stata 18 compatibility + Bayesian analysis commands |
| Q2 2026 | R/Python dual-output mode (Stata + equivalent R code) |
| Q3 2026 | Automated researcher assistant: writes methods sections |
| Q4 2026 | Full replication package generator from plain English |
Stop typing. Start thinking. Let the bridge handle the syntax.