Causal AI Scientist
0.1.2

Getting Started

  • Getting Started
    • Installation Guide
      • Prerequisites
      • Quick Start (Recommended)
      • Installation Methods
        • Method 1: pip (PyPI)
        • Method 2: Conda Environment
        • Method 4: Development Installation
      • Configuration Setup
      • Environment-Specific Instructions
        • Google Colab
        • Jupyter Notebook
      • Verification
      • Next Steps
        • Getting Additional Help
    • Quickstart Tutorial
      • Overview
      • Prerequisites
      • Step 1: Setup and Configuration
      • Step 2: Prepare Your Data
      • Step 3: Run Your First Analysis
      • Step 4: Understanding the Results
      • Step 5: Exploring Different Queries
      • Step 6: Working with Your Own Data
      • Common Use Cases
      • Understanding Method Selection
      • Next Steps
      • Troubleshooting Quick Fixes
    • Your First Causal Analysis
      • Understanding Causal Questions
        • What is Causal Inference?
        • Good vs. Poor Causal Questions
      • Step-by-Step Analysis Walkthrough
        • Step 1: Problem Setup
        • Step 2: Data Preparation
        • Step 3: Running the Analysis
        • Step 4: Understanding the Results
        • Step 5: Interpreting the Results
        • Step 6: Examining Method Selection
        • Step 7: Validating Results
      • Common Patterns and What They Mean
        • Different Types of Results
        • Understanding Confidence Intervals
      • Troubleshooting Common Issues
        • Data Quality Issues
        • Method Selection Issues
        • Result Interpretation Issues
      • Next Steps and Advanced Topics
    • What You’ll Learn
    • Prerequisites
    • Next Steps

User Guide

  • User Guide
    • Basic Usage
      • Core Workflow
      • Python API Usage
        • Single Analysis
        • Understanding Results
      • Command Line Interface
        • Single Analysis
      • Common Analysis Patterns
        • Experimental Data (RCT)
        • Observational Data
        • Time Series / Panel Data
        • Instrumental Variables
        • Regression Discontinuity
      • Working with Results
        • Extracting Key Information
        • Interpreting Diagnostics
      • Error Handling
        • Common Issues and Solutions
      • Best Practices
        • Data Preparation
        • Query Formulation
        • Result Interpretation
      • Next Steps
    • Advanced Usage
      • Advanced Configuration
        • Environment Variables
        • Programmatic Configuration
      • Method Selection Control
        • Understanding Automatic Selection
        • Influencing Method Selection
      • Custom Analysis Workflows
        • Multi-Method Comparison
        • Sensitivity Analysis
      • Integration Patterns
        • Jupyter Notebook Integration
        • Pipeline Integration
      • Custom Data Preprocessing
        • Data Validation and Cleaning
      • Performance Optimization
        • Caching Results
      • Best Practices for Advanced Usage
      • Next Steps
    • Batch Processing
      • Command Line Batch Processing
        • Basic Batch Analysis
        • Metadata CSV Format
        • Example Batch Command
      • Next Steps
    • Configuration
      • LLM Provider Configuration
        • Supported Providers
        • OpenAI Configuration
        • Anthropic Configuration
        • Together AI Configuration
      • Environment Configuration
        • Using .env Files
      • Next Steps
    • Guide Overview
    • Common Workflows
    • Best Practices

Tutorials & Examples

  • Tutorials & Examples
    • Interactive Notebooks
      • Notebook Categories
      • Running the Notebooks
      • Notebook Features
    • Case Studies
      • Education Policy Analysis: Learning Mindset Intervention
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Decision Tree Walkthrough
        • Method Exclusion Examples
        • Real-World Implications
        • Comparison with Alternative Approaches
        • Learning Objectives Achieved
        • Next Steps
      • Healthcare Treatment Effects: Hospital Treatment Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Decision Tree Alternative Scenarios
        • Clinical Implications
        • Comparison with Traditional Analysis
        • Learning Objectives Achieved
        • Next Steps
      • Marketing Campaign Evaluation: Instrumental Variables Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Decision Tree Alternative Scenarios
        • Business Implications
        • Comparison with Traditional Analysis
        • Learning Objectives Achieved
        • Next Steps
      • Economic Policy Impact: Minimum Wage Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Decision Tree Alternative Scenarios
        • Economic Interpretation
        • Learning Objectives Achieved
        • Next Steps
      • Technology Product Features: A/B Testing Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Business Decision Framework
        • Comparison with Traditional A/B Testing
        • Alternative Experimental Designs
        • Long-term Monitoring Strategy
        • Learning Objectives Achieved
        • Next Steps
      • Case Study Structure
      • Featured Case Studies
      • Learning Objectives
      • Datasets and Resources
    • Code Examples
      • Dataset Properties and Method Selection Gallery
        • Overview
        • Gallery Examples
        • Method Exclusion Examples
        • Dataset Property Decision Matrix
        • Common Decision Patterns
        • Next Steps
      • Decision Path Comparisons: Similar Datasets, Different Methods
        • Overview
        • Comparison 1: Randomized vs. Observational Education Data
        • Comparison 2: Cross-Sectional vs. Panel Policy Data
        • Comparison 3: Sharp vs. Fuzzy Discontinuity
        • Comparison 4: Strong vs. Weak Instrument
        • Comparison 5: Good vs. Poor Covariate Overlap
        • Key Learning Points
        • Next Steps
      • Example Categories
      • Quick Start Examples
      • Example Datasets
      • Usage Tips
      • Contributing Examples
    • Learning Path
    • Tutorial Categories

Causal Inference Methods

  • Causal Inference Methods
    • Overview of Causal Inference Methods
      • What is Causal Inference?
      • The Fundamental Problem of Causal Inference
      • Method Categories in Causal Agent
        • Experimental Methods
        • Quasi-Experimental Methods
        • Observational Methods
      • How Causal Agent Selects Methods
      • The Decision Tree Process
      • Method Assumptions and Validity
      • Best Practices
      • Getting Started
    • Method Selection Decision Tree
      • Complete Decision Tree Algorithm
      • Dataset Property Influence Visualization
      • Decision Criteria Explained
        • 1. Randomized Experiment Check
        • 2. Data Structure Analysis
        • 3. Instrumental Variable Assessment
        • 4. Treatment Variable Type
        • 5. Covariate Assessment
      • Step-by-Step Decision Walkthroughs
        • Walkthrough 1: Randomized Controlled Trial
        • Walkthrough 2: Panel Data Analysis
        • Walkthrough 6: Complex Multi-Treatment Scenario
        • Walkthrough 7: Weak Instrument Scenario
        • Edge Cases and Troubleshooting
        • Algorithm Robustness and Validation
        • Walkthrough 3: Regression Discontinuity
        • Walkthrough 4: Observational Study with Rich Covariates
        • Walkthrough 5: Instrumental Variables Analysis
      • Method Selection Examples
        • Example 1: A/B Test Analysis
        • Example 2: Policy Evaluation
        • Example 3: Observational Study
      • Decision Node Documentation
        • Node 1: Randomization Assessment
        • Node 2A: RCT Covariate Assessment
        • Node 2B: Data Structure Assessment
        • Node 3C: Treatment Variable Type Assessment
        • Node 4A: Instrumental Variable Assessment (Binary Treatment)
        • Node 5A: Covariate Richness Assessment
        • Node 6A: Covariate Overlap Assessment
        • Priority Ordering and Method Selection
      • Understanding Method Recommendations
        • Priority Ordering
        • Alternative Methods
      • Customizing Method Selection
        • Excluding Methods
        • Forcing Method Selection
      • Validating Method Choice
      • Interactive Tools and Utilities
        • Method Comparison Tool
        • Method Diagnostic Tool
      • Decision Tree Algorithm Implementation
      • Next Steps
    • Experimental Methods
      • Randomized Controlled Trials (RCT)
        • When to Use RCTs
        • Theoretical Background
        • Key Assumptions
        • Types of RCT Analysis
        • Implementation in CAIS
        • Diagnostic Tests and Validation
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Educational Intervention RCT
        • Further Reading
      • Overview
      • Method Details
      • Implementation in CAIS
      • Best Practices
      • Common Challenges
    • Quasi-Experimental Methods
      • Difference-in-Differences (DiD)
        • When to Use DiD
        • Theoretical Background
        • Key Assumptions
        • Implementation in CAIS
        • Diagnostic Tests and Validation
        • Advanced DiD Methods
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Minimum Wage Policy Evaluation
        • Extensions and Related Methods
        • Further Reading
      • Instrumental Variables (IV)
        • When to Use IV
        • Theoretical Background
        • Key Assumptions
        • Types of IV Estimands
        • Implementation in Causal Agent
        • Diagnostic Tests and Validation
        • Common IV Applications
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Returns to Education
        • Advanced IV Methods
        • Further Reading
      • Regression Discontinuity Design (RDD)
        • When to Use RDD
        • Theoretical Background
        • Key Assumptions
        • Types of RDD
        • Implementation in Causal Agent
        • Diagnostic Tests and Validation
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Educational Remediation Program
        • Advanced RDD Methods
        • Further Reading
      • Overview
      • Method Details
      • Implementation in Causal Agent
      • Assumption Validation
      • Best Practices
      • Common Pitfalls
    • Observational Methods
      • Propensity Score Matching
        • When to Use PSM
        • Theoretical Background
        • Key Assumptions
        • Types of Matching
        • Implementation in Causal Agent
        • Diagnostic Tests and Validation
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Job Training Program Evaluation
        • Extensions and Related Methods
        • Further Reading
      • Overview
      • Method Details
      • Implementation in Causal Agent
      • Assumption Validation
      • Balance Assessment
      • Best Practices
      • Sensitivity Analysis
      • Common Challenges
      • Advanced Topics
    • Method Selection Guide
    • Method Comparison
    • Choosing the Right Method

Theoretical Background

  • Theoretical Background
    • Causal Inference Basics
      • What is Causal Inference?
      • The Fundamental Problem
      • How Automated Systems Approach This Problem
      • Key Concepts for Automated Analysis
        • Confounding
        • Selection Bias
        • Treatment Assignment Mechanisms
      • The Agent’s Decision-Making Process
      • Types of Causal Questions
      • Common Misconceptions
      • Why Automated Causal Analysis Matters
      • Next Steps
    • Agent Architecture and Decision-Making Process
      • Overview of the Autonomous Agent
      • Agent Workflow: Step-by-Step Process
        • 1. Initial Data Analysis
        • 2. Variable Identification
        • 3. Treatment Assignment Analysis
        • 4. Decision Tree Navigation
        • 5. Method Selection and Prioritization
        • 6. Assumption Testing and Validation
        • 7. Effect Estimation
        • 8. Result Interpretation and Communication
      • LLM Integration Architecture
        • Data Understanding Prompts
        • Method Selection Prompts
        • Result Interpretation Prompts
      • Error Handling and Recovery
      • Agent Limitations and Human Oversight
      • Continuous Learning and Improvement
    • LLM Integration in Causal Analysis
      • Why LLMs in Causal Inference?
      • LLM Integration Architecture
      • Stage 1: Data Understanding and Variable Identification
      • Stage 2: Method Selection and Prioritization
      • Stage 3: Assumption Testing and Validation
      • Stage 4: Result Interpretation and Communication
      • Advanced LLM Integration Features
      • Quality Assurance and Validation
      • Limitations and Future Directions
    • Method Selection and Decision-Making
      • The Method Selection Challenge
      • The Agent’s Decision Framework
        • Decision Tree Overview
      • Stage 1: Treatment Assignment Analysis
        • Random Assignment (Experimental)
        • As-Good-As-Random Assignment (Quasi-Experimental)
        • Non-Random Assignment (Observational)
      • Stage 2: Data Structure Analysis
        • Panel Data Detection
        • Cross-Sectional Data
        • Time Series Data
      • Stage 3: Method Prioritization
        • Identification Strength Hierarchy
        • Assumption Assessment
      • Stage 4: Robustness and Sensitivity Analysis
        • Multiple Method Implementation
        • Assumption Testing Protocol
      • Stage 5: Method Selection Decision
        • Decision Integration
        • Communicating Uncertainty
      • Common Decision Scenarios
      • Best Practices for Users
    • Result Interpretation and Communication
      • The Challenge of Causal Interpretation
      • Understanding Causal Effect Estimates
        • Types of Causal Effects
        • Effect Magnitudes and Practical Significance
        • Confidence Intervals and Uncertainty
      • Method-Specific Interpretation Considerations
        • Randomized Experiments
        • Difference-in-Differences
        • Instrumental Variables
        • Regression Discontinuity
        • Propensity Score Methods
      • Communicating Limitations and Assumptions
        • Assumption Violations
        • External Validity
      • Tailoring Communication to Different Audiences
        • Academic Audiences
        • Policy Makers
        • General Public
        • Stakeholders and Practitioners
      • Handling Negative or Null Results
      • Best Practices for Result Interpretation
    • Glossary
      • A
      • B
      • C
      • D
      • E
      • F
      • I
      • L
      • M
      • N
      • O
      • P
      • Q
      • R
      • S
      • T
      • U
      • V
      • Common Acronyms
      • Statistical Terms
      • AI and Machine Learning Terms
      • Research Design Terms
      • Policy Evaluation Terms
    • Learning Path
    • Key Concepts
    • Common Pitfalls and How the Agent Addresses Them
    • Agent Capabilities and Limitations

API Reference

  • API Reference
    • Module Reference
      • causal_agent
        • causal_agent.analyze_dataset
        • causal_agent.create_workflow_state_update
        • causal_agent.format_output
        • causal_agent.generate_explanation
        • causal_agent.interpret_query
        • causal_agent.parse_input
        • causal_agent.run_causal_analysis
        • causal_agent.validate_method
        • run_causal_analysis()
      • causal_agent.components package
        • parse_input()
        • analyze_dataset()
        • interpret_query()
        • select_method()
        • validate_method()
        • generate_explanation()
        • format_output()
        • create_workflow_state_update()
        • Submodules
        • causal_agent.components.dataset_analyzer module
        • causal_agent.components.decision_tree module
        • causal_agent.components.decision_tree_llm module
        • causal_agent.components.explanation_generator module
        • causal_agent.components.input_parser module
        • causal_agent.components.method_validator module
        • causal_agent.components.output_formatter module
        • causal_agent.components.query_interpreter module
        • causal_agent.components.state_manager module
      • causal_agent.tools package
        • input_parser_tool()
        • dataset_analyzer_tool()
        • query_interpreter_tool()
        • method_selector_tool()
        • method_validator_tool()
        • method_executor_tool()
        • explanation_generator_tool()
        • output_formatter_tool()
        • Submodules
        • causal_agent.tools.data_analyzer module
        • causal_agent.tools.dataset_analyzer_tool module
        • causal_agent.tools.explanation_generator_tool module
        • causal_agent.tools.input_parser_tool module
        • causal_agent.tools.method_executor_tool module
        • causal_agent.tools.method_selector_tool module
        • causal_agent.tools.method_validator_tool module
        • causal_agent.tools.output_formatter_tool module
        • causal_agent.tools.query_interpreter_tool module
      • causal_agent.utils package
        • Submodules
        • causal_agent.utils.agent module
        • causal_agent.utils.llm_helpers module
      • causal_agent.methods package
        • CausalMethod
        • psm_estimate_effect()
        • psw_estimate_effect()
        • iv_estimate_effect()
        • did_estimate_effect()
        • rdd_estimate_effect()
        • dim_estimate_effect()
        • lr_estimate_effect()
        • ba_estimate_effect()
        • estimate_effect_gps()
        • Submodules
        • causal_agent.methods.causal_method module
        • causal_agent.methods.utils module
        • Subpackages
      • Core Modules
      • Auto-Generated Documentation
      • Navigation
      • Code Examples
    • API Overview
      • causal_agent
        • causal_agent.analyze_dataset
        • causal_agent.create_workflow_state_update
        • causal_agent.format_output
        • causal_agent.generate_explanation
        • causal_agent.interpret_query
        • causal_agent.parse_input
        • causal_agent.run_causal_analysis
        • causal_agent.validate_method
        • run_causal_analysis()
    • Quick Reference
    • Cross-References and Links
    • Navigation Tips

Development

  • Development
    • Architecture
      • System Overview
      • High-Level Architecture
      • Agent Workflow
      • Component Architecture
        • Core Components
        • Analysis Components
      • Tool Architecture
      • Method Implementation Architecture
      • LLM Integration Architecture
      • Data Flow Architecture
      • Testing Architecture
      • Extension Points
      • Performance Considerations
      • Security and Privacy
    • Extending Methods
      • Overview
      • Method Implementation Structure
        • Base Method Interface
        • Method Categories
      • Step-by-Step Method Implementation
        • Step 1: Create Method Implementation
        • Step 2: Create Method-Specific Components
        • Step 3: Update Decision Tree Logic
        • Step 4: Update Method Executor
        • Step 5: Create LLM Integration
        • Step 6: Create Comprehensive Tests
        • Step 7: Integration Tests
        • Step 8: Create Synthetic Data for Testing
        • Step 9: Documentation
      • Testing Your Implementation
        • Comprehensive Testing Strategy
        • Running Tests
        • Validation Checklist
      • Best Practices
        • Code Quality
        • Statistical Rigor
        • Integration
      • Common Pitfalls
      • Getting Help
    • LLM Integration
      • Overview
      • LLM Provider Architecture
        • Supported Providers
        • Configuration Management
        • Environment Configuration
      • Prompt Engineering Patterns
        • Core Prompt Structure
        • Variable Identification Prompts
        • Method Selection Prompts
        • Result Interpretation Prompts
      • Response Processing Architecture
        • Structured Output Parsing
        • Response Validation Schemas
        • Error Handling and Retry Logic
      • Prompt Optimization Strategies
        • Few-Shot Learning
        • Chain-of-Thought Reasoning
        • Prompt Versioning and A/B Testing
      • Integration with Decision Tree
        • LLM-Enhanced Decision Logic
      • Performance Optimization
        • Caching Strategies
        • Batch Processing
      • Monitoring and Debugging
        • LLM Call Logging
      • Testing LLM Integration
        • Mock LLM Responses
        • Integration Testing
      • Best Practices
        • Prompt Design
        • Error Handling
        • Performance
        • Security
    • Synthetic Data Generation System
      • Overview
      • System Architecture and Decision Tree Integration
        • Decision Tree Validation Through Synthetic Data
      • Data Generation Framework
        • Core Components
        • Configuration System
        • Base Data Generator Architecture
        • Base Data Generator
      • Method-Specific Generators and Decision Tree Testing
        • Randomized Controlled Trial (RCT) Generator
        • Multi-Treatment RCT Generator
        • Difference-in-Differences (DiD) Generators
        • Instrumental Variables (IV) Generators
        • Regression Discontinuity (RDD) Generator
        • Propensity Score Generators
        • Front-Door Criterion Generator
        • Difference-in-Differences Generator
        • Instrumental Variables Generator
        • Regression Discontinuity Generator
        • Propensity Score Generator
      • Data Generation Workflow and Scripts
        • Generation Pipeline Overview
        • Step 1: Configuration and Parameter Setup
        • Step 2: Raw Data Generation
        • Step 3: Context Generation with LLM Integration
        • Step 4: Data Finalization and Integration
        • Logging and Quality Control
        • Batch Processing and Agent Testing
      • Scenario Generation and Testing
        • Assumption Violation Scenarios
        • Edge Case and Robustness Testing
      • Usage Examples and Best Practices
        • Complete Workflow Example
        • Batch Testing Example
        • Best Practices for Synthetic Data Generation
      • Integration with CAIS Testing Framework
        • Continuous Integration Testing
        • Performance Benchmarking
        • Quality Assurance and Validation
      • Future Enhancements and Extensions
        • Planned Improvements
        • Contributing to the Synthetic Data System
      • Conclusion
        • Data Validation
      • Testing Integration
        • Using Synthetic Data in Tests
        • Continuous Integration
      • Best Practices
        • Data Generation Guidelines
        • Validation Standards
        • Testing Integration
    • Testing Framework
      • Overview
      • Test Organization
        • Directory Structure
      • Base Test Infrastructure
        • Base Test Classes
        • Pytest Configuration
      • Unit Testing
        • Component Unit Tests
        • Method Unit Tests
        • Tool Unit Tests
      • Integration Testing
        • Workflow Integration Tests
        • LLM Integration Tests
      • End-to-End Testing
        • Complete Workflow Tests
      • Performance Testing
        • Scalability Tests
        • Method Performance Tests
      • Test Automation and CI/CD
        • GitHub Actions Configuration
        • Test Coverage and Quality
      • Best Practices
        • Test Design Principles
        • Data Management
        • Continuous Integration
    • Getting Started with Development
    • Development Workflow
    • Project Structure
    • Contribution Areas
    • Development Standards

About

  • About CAIS
    • Citation Information
      • BibTeX Format
      • License and Usage Terms
      • Contact for Citation Questions
    • Changelog
      • Version 0.1.2 (Current)
      • Version 0.1.1
      • Version 0.1.0
      • Contributing to Changelog
      • Release Process
      • Contact for Release Information
    • License and Usage Terms
      • MIT License
      • What This Means
      • Usage Guidelines
      • Third-Party Dependencies
      • Data and Model Licenses
      • Contributing and License
      • License Compliance
      • Frequently Asked Questions
      • Contact for License Questions
      • License History
    • Project Overview
Causal AI Scientist
0.1.2
🚀 Getting Started 🔬 Methods 📖 Tutorials 📚 API

Getting Started

  • Getting Started
    • Installation Guide
      • Prerequisites
      • Quick Start (Recommended)
      • Installation Methods
        • Method 1: pip (PyPI)
        • Method 2: Conda Environment
        • Method 4: Development Installation
      • Configuration Setup
      • Environment-Specific Instructions
        • Google Colab
        • Jupyter Notebook
      • Verification
      • Next Steps
        • Getting Additional Help
    • Quickstart Tutorial
      • Overview
      • Prerequisites
      • Step 1: Setup and Configuration
      • Step 2: Prepare Your Data
      • Step 3: Run Your First Analysis
      • Step 4: Understanding the Results
      • Step 5: Exploring Different Queries
      • Step 6: Working with Your Own Data
      • Common Use Cases
      • Understanding Method Selection
      • Next Steps
      • Troubleshooting Quick Fixes
    • Your First Causal Analysis
      • Understanding Causal Questions
        • What is Causal Inference?
        • Good vs. Poor Causal Questions
      • Step-by-Step Analysis Walkthrough
        • Step 1: Problem Setup
        • Step 2: Data Preparation
        • Step 3: Running the Analysis
        • Step 4: Understanding the Results
        • Step 5: Interpreting the Results
        • Step 6: Examining Method Selection
        • Step 7: Validating Results
      • Common Patterns and What They Mean
        • Different Types of Results
        • Understanding Confidence Intervals
      • Troubleshooting Common Issues
        • Data Quality Issues
        • Method Selection Issues
        • Result Interpretation Issues
      • Next Steps and Advanced Topics
    • What You’ll Learn
    • Prerequisites
    • Next Steps

User Guide

  • User Guide
    • Basic Usage
      • Core Workflow
      • Python API Usage
        • Single Analysis
        • Understanding Results
      • Command Line Interface
        • Single Analysis
      • Common Analysis Patterns
        • Experimental Data (RCT)
        • Observational Data
        • Time Series / Panel Data
        • Instrumental Variables
        • Regression Discontinuity
      • Working with Results
        • Extracting Key Information
        • Interpreting Diagnostics
      • Error Handling
        • Common Issues and Solutions
      • Best Practices
        • Data Preparation
        • Query Formulation
        • Result Interpretation
      • Next Steps
    • Advanced Usage
      • Advanced Configuration
        • Environment Variables
        • Programmatic Configuration
      • Method Selection Control
        • Understanding Automatic Selection
        • Influencing Method Selection
      • Custom Analysis Workflows
        • Multi-Method Comparison
        • Sensitivity Analysis
      • Integration Patterns
        • Jupyter Notebook Integration
        • Pipeline Integration
      • Custom Data Preprocessing
        • Data Validation and Cleaning
      • Performance Optimization
        • Caching Results
      • Best Practices for Advanced Usage
      • Next Steps
    • Batch Processing
      • Command Line Batch Processing
        • Basic Batch Analysis
        • Metadata CSV Format
        • Example Batch Command
      • Next Steps
    • Configuration
      • LLM Provider Configuration
        • Supported Providers
        • OpenAI Configuration
        • Anthropic Configuration
        • Together AI Configuration
      • Environment Configuration
        • Using .env Files
      • Next Steps
    • Guide Overview
    • Common Workflows
    • Best Practices

Tutorials & Examples

  • Tutorials & Examples
    • Interactive Notebooks
      • Notebook Categories
      • Running the Notebooks
      • Notebook Features
    • Case Studies
      • Education Policy Analysis: Learning Mindset Intervention
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Decision Tree Walkthrough
        • Method Exclusion Examples
        • Real-World Implications
        • Comparison with Alternative Approaches
        • Learning Objectives Achieved
        • Next Steps
      • Healthcare Treatment Effects: Hospital Treatment Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Decision Tree Alternative Scenarios
        • Clinical Implications
        • Comparison with Traditional Analysis
        • Learning Objectives Achieved
        • Next Steps
      • Marketing Campaign Evaluation: Instrumental Variables Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Decision Tree Alternative Scenarios
        • Business Implications
        • Comparison with Traditional Analysis
        • Learning Objectives Achieved
        • Next Steps
      • Economic Policy Impact: Minimum Wage Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Decision Tree Alternative Scenarios
        • Economic Interpretation
        • Learning Objectives Achieved
        • Next Steps
      • Technology Product Features: A/B Testing Analysis
        • Problem Statement
        • Dataset Overview
        • Agent Decision-Making Process
        • Method Exclusion Examples
        • Robustness Analysis
        • Business Decision Framework
        • Comparison with Traditional A/B Testing
        • Alternative Experimental Designs
        • Long-term Monitoring Strategy
        • Learning Objectives Achieved
        • Next Steps
      • Case Study Structure
      • Featured Case Studies
      • Learning Objectives
      • Datasets and Resources
    • Code Examples
      • Dataset Properties and Method Selection Gallery
        • Overview
        • Gallery Examples
        • Method Exclusion Examples
        • Dataset Property Decision Matrix
        • Common Decision Patterns
        • Next Steps
      • Decision Path Comparisons: Similar Datasets, Different Methods
        • Overview
        • Comparison 1: Randomized vs. Observational Education Data
        • Comparison 2: Cross-Sectional vs. Panel Policy Data
        • Comparison 3: Sharp vs. Fuzzy Discontinuity
        • Comparison 4: Strong vs. Weak Instrument
        • Comparison 5: Good vs. Poor Covariate Overlap
        • Key Learning Points
        • Next Steps
      • Example Categories
      • Quick Start Examples
      • Example Datasets
      • Usage Tips
      • Contributing Examples
    • Learning Path
    • Tutorial Categories

Causal Inference Methods

  • Causal Inference Methods
    • Overview of Causal Inference Methods
      • What is Causal Inference?
      • The Fundamental Problem of Causal Inference
      • Method Categories in Causal Agent
        • Experimental Methods
        • Quasi-Experimental Methods
        • Observational Methods
      • How Causal Agent Selects Methods
      • The Decision Tree Process
      • Method Assumptions and Validity
      • Best Practices
      • Getting Started
    • Method Selection Decision Tree
      • Complete Decision Tree Algorithm
      • Dataset Property Influence Visualization
      • Decision Criteria Explained
        • 1. Randomized Experiment Check
        • 2. Data Structure Analysis
        • 3. Instrumental Variable Assessment
        • 4. Treatment Variable Type
        • 5. Covariate Assessment
      • Step-by-Step Decision Walkthroughs
        • Walkthrough 1: Randomized Controlled Trial
        • Walkthrough 2: Panel Data Analysis
        • Walkthrough 6: Complex Multi-Treatment Scenario
        • Walkthrough 7: Weak Instrument Scenario
        • Edge Cases and Troubleshooting
        • Algorithm Robustness and Validation
        • Walkthrough 3: Regression Discontinuity
        • Walkthrough 4: Observational Study with Rich Covariates
        • Walkthrough 5: Instrumental Variables Analysis
      • Method Selection Examples
        • Example 1: A/B Test Analysis
        • Example 2: Policy Evaluation
        • Example 3: Observational Study
      • Decision Node Documentation
        • Node 1: Randomization Assessment
        • Node 2A: RCT Covariate Assessment
        • Node 2B: Data Structure Assessment
        • Node 3C: Treatment Variable Type Assessment
        • Node 4A: Instrumental Variable Assessment (Binary Treatment)
        • Node 5A: Covariate Richness Assessment
        • Node 6A: Covariate Overlap Assessment
        • Priority Ordering and Method Selection
      • Understanding Method Recommendations
        • Priority Ordering
        • Alternative Methods
      • Customizing Method Selection
        • Excluding Methods
        • Forcing Method Selection
      • Validating Method Choice
      • Interactive Tools and Utilities
        • Method Comparison Tool
        • Method Diagnostic Tool
      • Decision Tree Algorithm Implementation
      • Next Steps
    • Experimental Methods
      • Randomized Controlled Trials (RCT)
        • When to Use RCTs
        • Theoretical Background
        • Key Assumptions
        • Types of RCT Analysis
        • Implementation in CAIS
        • Diagnostic Tests and Validation
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Educational Intervention RCT
        • Further Reading
      • Overview
      • Method Details
      • Implementation in CAIS
      • Best Practices
      • Common Challenges
    • Quasi-Experimental Methods
      • Difference-in-Differences (DiD)
        • When to Use DiD
        • Theoretical Background
        • Key Assumptions
        • Implementation in CAIS
        • Diagnostic Tests and Validation
        • Advanced DiD Methods
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Minimum Wage Policy Evaluation
        • Extensions and Related Methods
        • Further Reading
      • Instrumental Variables (IV)
        • When to Use IV
        • Theoretical Background
        • Key Assumptions
        • Types of IV Estimands
        • Implementation in Causal Agent
        • Diagnostic Tests and Validation
        • Common IV Applications
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Returns to Education
        • Advanced IV Methods
        • Further Reading
      • Regression Discontinuity Design (RDD)
        • When to Use RDD
        • Theoretical Background
        • Key Assumptions
        • Types of RDD
        • Implementation in Causal Agent
        • Diagnostic Tests and Validation
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Educational Remediation Program
        • Advanced RDD Methods
        • Further Reading
      • Overview
      • Method Details
      • Implementation in Causal Agent
      • Assumption Validation
      • Best Practices
      • Common Pitfalls
    • Observational Methods
      • Propensity Score Matching
        • When to Use PSM
        • Theoretical Background
        • Key Assumptions
        • Types of Matching
        • Implementation in Causal Agent
        • Diagnostic Tests and Validation
        • Best Practices
        • Common Pitfalls and Solutions
        • Example: Job Training Program Evaluation
        • Extensions and Related Methods
        • Further Reading
      • Overview
      • Method Details
      • Implementation in Causal Agent
      • Assumption Validation
      • Balance Assessment
      • Best Practices
      • Sensitivity Analysis
      • Common Challenges
      • Advanced Topics
    • Method Selection Guide
    • Method Comparison
    • Choosing the Right Method

Theoretical Background

  • Theoretical Background
    • Causal Inference Basics
      • What is Causal Inference?
      • The Fundamental Problem
      • How Automated Systems Approach This Problem
      • Key Concepts for Automated Analysis
        • Confounding
        • Selection Bias
        • Treatment Assignment Mechanisms
      • The Agent’s Decision-Making Process
      • Types of Causal Questions
      • Common Misconceptions
      • Why Automated Causal Analysis Matters
      • Next Steps
    • Agent Architecture and Decision-Making Process
      • Overview of the Autonomous Agent
      • Agent Workflow: Step-by-Step Process
        • 1. Initial Data Analysis
        • 2. Variable Identification
        • 3. Treatment Assignment Analysis
        • 4. Decision Tree Navigation
        • 5. Method Selection and Prioritization
        • 6. Assumption Testing and Validation
        • 7. Effect Estimation
        • 8. Result Interpretation and Communication
      • LLM Integration Architecture
        • Data Understanding Prompts
        • Method Selection Prompts
        • Result Interpretation Prompts
      • Error Handling and Recovery
      • Agent Limitations and Human Oversight
      • Continuous Learning and Improvement
    • LLM Integration in Causal Analysis
      • Why LLMs in Causal Inference?
      • LLM Integration Architecture
      • Stage 1: Data Understanding and Variable Identification
      • Stage 2: Method Selection and Prioritization
      • Stage 3: Assumption Testing and Validation
      • Stage 4: Result Interpretation and Communication
      • Advanced LLM Integration Features
      • Quality Assurance and Validation
      • Limitations and Future Directions
    • Method Selection and Decision-Making
      • The Method Selection Challenge
      • The Agent’s Decision Framework
        • Decision Tree Overview
      • Stage 1: Treatment Assignment Analysis
        • Random Assignment (Experimental)
        • As-Good-As-Random Assignment (Quasi-Experimental)
        • Non-Random Assignment (Observational)
      • Stage 2: Data Structure Analysis
        • Panel Data Detection
        • Cross-Sectional Data
        • Time Series Data
      • Stage 3: Method Prioritization
        • Identification Strength Hierarchy
        • Assumption Assessment
      • Stage 4: Robustness and Sensitivity Analysis
        • Multiple Method Implementation
        • Assumption Testing Protocol
      • Stage 5: Method Selection Decision
        • Decision Integration
        • Communicating Uncertainty
      • Common Decision Scenarios
      • Best Practices for Users
    • Result Interpretation and Communication
      • The Challenge of Causal Interpretation
      • Understanding Causal Effect Estimates
        • Types of Causal Effects
        • Effect Magnitudes and Practical Significance
        • Confidence Intervals and Uncertainty
      • Method-Specific Interpretation Considerations
        • Randomized Experiments
        • Difference-in-Differences
        • Instrumental Variables
        • Regression Discontinuity
        • Propensity Score Methods
      • Communicating Limitations and Assumptions
        • Assumption Violations
        • External Validity
      • Tailoring Communication to Different Audiences
        • Academic Audiences
        • Policy Makers
        • General Public
        • Stakeholders and Practitioners
      • Handling Negative or Null Results
      • Best Practices for Result Interpretation
    • Glossary
      • A
      • B
      • C
      • D
      • E
      • F
      • I
      • L
      • M
      • N
      • O
      • P
      • Q
      • R
      • S
      • T
      • U
      • V
      • Common Acronyms
      • Statistical Terms
      • AI and Machine Learning Terms
      • Research Design Terms
      • Policy Evaluation Terms
    • Learning Path
    • Key Concepts
    • Common Pitfalls and How the Agent Addresses Them
    • Agent Capabilities and Limitations

API Reference

  • API Reference
    • Module Reference
      • causal_agent
        • causal_agent.analyze_dataset
        • causal_agent.create_workflow_state_update
        • causal_agent.format_output
        • causal_agent.generate_explanation
        • causal_agent.interpret_query
        • causal_agent.parse_input
        • causal_agent.run_causal_analysis
        • causal_agent.validate_method
        • run_causal_analysis()
      • causal_agent.components package
        • parse_input()
        • analyze_dataset()
        • interpret_query()
        • select_method()
        • validate_method()
        • generate_explanation()
        • format_output()
        • create_workflow_state_update()
        • Submodules
        • causal_agent.components.dataset_analyzer module
        • causal_agent.components.decision_tree module
        • causal_agent.components.decision_tree_llm module
        • causal_agent.components.explanation_generator module
        • causal_agent.components.input_parser module
        • causal_agent.components.method_validator module
        • causal_agent.components.output_formatter module
        • causal_agent.components.query_interpreter module
        • causal_agent.components.state_manager module
      • causal_agent.tools package
        • input_parser_tool()
        • dataset_analyzer_tool()
        • query_interpreter_tool()
        • method_selector_tool()
        • method_validator_tool()
        • method_executor_tool()
        • explanation_generator_tool()
        • output_formatter_tool()
        • Submodules
        • causal_agent.tools.data_analyzer module
        • causal_agent.tools.dataset_analyzer_tool module
        • causal_agent.tools.explanation_generator_tool module
        • causal_agent.tools.input_parser_tool module
        • causal_agent.tools.method_executor_tool module
        • causal_agent.tools.method_selector_tool module
        • causal_agent.tools.method_validator_tool module
        • causal_agent.tools.output_formatter_tool module
        • causal_agent.tools.query_interpreter_tool module
      • causal_agent.utils package
        • Submodules
        • causal_agent.utils.agent module
        • causal_agent.utils.llm_helpers module
      • causal_agent.methods package
        • CausalMethod
        • psm_estimate_effect()
        • psw_estimate_effect()
        • iv_estimate_effect()
        • did_estimate_effect()
        • rdd_estimate_effect()
        • dim_estimate_effect()
        • lr_estimate_effect()
        • ba_estimate_effect()
        • estimate_effect_gps()
        • Submodules
        • causal_agent.methods.causal_method module
        • causal_agent.methods.utils module
        • Subpackages
      • Core Modules
      • Auto-Generated Documentation
      • Navigation
      • Code Examples
    • API Overview
      • causal_agent
        • causal_agent.analyze_dataset
        • causal_agent.create_workflow_state_update
        • causal_agent.format_output
        • causal_agent.generate_explanation
        • causal_agent.interpret_query
        • causal_agent.parse_input
        • causal_agent.run_causal_analysis
        • causal_agent.validate_method
        • run_causal_analysis()
    • Quick Reference
    • Cross-References and Links
    • Navigation Tips

Development

  • Development
    • Architecture
      • System Overview
      • High-Level Architecture
      • Agent Workflow
      • Component Architecture
        • Core Components
        • Analysis Components
      • Tool Architecture
      • Method Implementation Architecture
      • LLM Integration Architecture
      • Data Flow Architecture
      • Testing Architecture
      • Extension Points
      • Performance Considerations
      • Security and Privacy
    • Extending Methods
      • Overview
      • Method Implementation Structure
        • Base Method Interface
        • Method Categories
      • Step-by-Step Method Implementation
        • Step 1: Create Method Implementation
        • Step 2: Create Method-Specific Components
        • Step 3: Update Decision Tree Logic
        • Step 4: Update Method Executor
        • Step 5: Create LLM Integration
        • Step 6: Create Comprehensive Tests
        • Step 7: Integration Tests
        • Step 8: Create Synthetic Data for Testing
        • Step 9: Documentation
      • Testing Your Implementation
        • Comprehensive Testing Strategy
        • Running Tests
        • Validation Checklist
      • Best Practices
        • Code Quality
        • Statistical Rigor
        • Integration
      • Common Pitfalls
      • Getting Help
    • LLM Integration
      • Overview
      • LLM Provider Architecture
        • Supported Providers
        • Configuration Management
        • Environment Configuration
      • Prompt Engineering Patterns
        • Core Prompt Structure
        • Variable Identification Prompts
        • Method Selection Prompts
        • Result Interpretation Prompts
      • Response Processing Architecture
        • Structured Output Parsing
        • Response Validation Schemas
        • Error Handling and Retry Logic
      • Prompt Optimization Strategies
        • Few-Shot Learning
        • Chain-of-Thought Reasoning
        • Prompt Versioning and A/B Testing
      • Integration with Decision Tree
        • LLM-Enhanced Decision Logic
      • Performance Optimization
        • Caching Strategies
        • Batch Processing
      • Monitoring and Debugging
        • LLM Call Logging
      • Testing LLM Integration
        • Mock LLM Responses
        • Integration Testing
      • Best Practices
        • Prompt Design
        • Error Handling
        • Performance
        • Security
    • Synthetic Data Generation System
      • Overview
      • System Architecture and Decision Tree Integration
        • Decision Tree Validation Through Synthetic Data
      • Data Generation Framework
        • Core Components
        • Configuration System
        • Base Data Generator Architecture
        • Base Data Generator
      • Method-Specific Generators and Decision Tree Testing
        • Randomized Controlled Trial (RCT) Generator
        • Multi-Treatment RCT Generator
        • Difference-in-Differences (DiD) Generators
        • Instrumental Variables (IV) Generators
        • Regression Discontinuity (RDD) Generator
        • Propensity Score Generators
        • Front-Door Criterion Generator
        • Difference-in-Differences Generator
        • Instrumental Variables Generator
        • Regression Discontinuity Generator
        • Propensity Score Generator
      • Data Generation Workflow and Scripts
        • Generation Pipeline Overview
        • Step 1: Configuration and Parameter Setup
        • Step 2: Raw Data Generation
        • Step 3: Context Generation with LLM Integration
        • Step 4: Data Finalization and Integration
        • Logging and Quality Control
        • Batch Processing and Agent Testing
      • Scenario Generation and Testing
        • Assumption Violation Scenarios
        • Edge Case and Robustness Testing
      • Usage Examples and Best Practices
        • Complete Workflow Example
        • Batch Testing Example
        • Best Practices for Synthetic Data Generation
      • Integration with CAIS Testing Framework
        • Continuous Integration Testing
        • Performance Benchmarking
        • Quality Assurance and Validation
      • Future Enhancements and Extensions
        • Planned Improvements
        • Contributing to the Synthetic Data System
      • Conclusion
        • Data Validation
      • Testing Integration
        • Using Synthetic Data in Tests
        • Continuous Integration
      • Best Practices
        • Data Generation Guidelines
        • Validation Standards
        • Testing Integration
    • Testing Framework
      • Overview
      • Test Organization
        • Directory Structure
      • Base Test Infrastructure
        • Base Test Classes
        • Pytest Configuration
      • Unit Testing
        • Component Unit Tests
        • Method Unit Tests
        • Tool Unit Tests
      • Integration Testing
        • Workflow Integration Tests
        • LLM Integration Tests
      • End-to-End Testing
        • Complete Workflow Tests
      • Performance Testing
        • Scalability Tests
        • Method Performance Tests
      • Test Automation and CI/CD
        • GitHub Actions Configuration
        • Test Coverage and Quality
      • Best Practices
        • Test Design Principles
        • Data Management
        • Continuous Integration
    • Getting Started with Development
    • Development Workflow
    • Project Structure
    • Contribution Areas
    • Development Standards

About

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