Causal Agent Documentation

Welcome to the Causal-Agent is an intelligent system that helps researchers and practitioners conduct rigorous causal inference analysis by automatically selecting appropriate causal inference methods, validating assumptions, and providing interpretable results.

Note

Causal Agent is designed to democratize causal inference by making advanced statistical methods accessible to users with varying levels of statistical expertise.

What is causal-agent?

The Causal Agent is an AI-powered tool that:

  • Automatically selects the most appropriate causal inference method based on your data and research question

  • Validates assumptions required for causal identification and provides diagnostic feedback

  • Generates interpretable results with clear explanations of causal effects and their limitations

  • Supports multiple methods including RCTs, Difference-in-Differences, Instrumental Variables, Regression Discontinuity, and Propensity Score methods

  • Provides theoretical guidance to help users understand the causal inference process

Key Features

🔍 Intelligent Method Selection

Uses decision tree logic to recommend the best causal inference approach for your specific research context.

📊 Comprehensive Method Support

Supports experimental, quasi-experimental, and observational study designs with automated diagnostics.

🤖 LLM-Powered Analysis

Leverages large language models to provide contextual explanations and interpret results in plain language.

📈 Robust Validation

Automatically checks assumptions and provides diagnostic tests to ensure the validity of causal conclusions.

🎯 Domain Flexibility

Works across various domains including economics, healthcare, education, and social sciences.

Quick Start

Get started with Causal Agent in just a few steps:

  1. Install Causal Agent: pip install causal-agent

  2. Load your data: Import your dataset in CSV or pandas DataFrame format

  3. Run analysis: Let Causal Agent automatically select and execute the appropriate causal inference method

  4. Interpret results: Review the generated causal effect estimates and explanations

from causal_agent import CausalAgent

# Initialize the agent
agent = CausalAgent()

# Run causal analysis
result = agent.analyze(
    data="your_dataset.csv",
    treatment="treatment_variable",
    outcome="outcome_variable"
)

# View results
print(result.summary())

Documentation Structure

Getting Started

User Guide

Tutorials & Examples

Causal Inference Methods

Theoretical Background

API Reference

Development

About

Community & Support

  • GitHub Repository: causal-agent

  • Issue Tracker: Report bugs and request features

  • Discussions: Join the community discussions

  • Contributing: See our development/contributing guide

Citation

If you use Causal Agent in your research, please cite:

@software{causal_agent,
  title={Causal Agent: Automated Causal Inference Analysis},
  author={vishal verma, sawal acharya, samuel simko, devansh bhardwaj, anahita haghighat, zhijing jin},
  year={2025},
  url={https://github.com/causalNLP/causal-agent}
}

Indices and Tables