CausalLab

Documentation
Examples
Getting Started
  • What is CausalLab?
  • Projects vs Templates
  • Quick Start Guide
  • Creating Your First Project
Core Modules
  • Causal Discovery Lab
  • Identification Strategy Optimizer
  • Mechanism Path Analyzer
  • Policy Simulation Sandbox
Methodology
  • Causal Inference Basics
  • Identification Strategies
  • Mediation Analysis
  • Counterfactual Simulation
Workflows
  • Template-Based Research
  • From Project to Template
  • Collaborative Research
  • Exporting Results

Documentation

Complete guide to using CausalLab for AI-powered causal inference research

What is CausalLab?

CausalLab is an AI-powered research platform designed for social scientists, economists, and management researchers. It automates complex causal inference workflows, from discovering causal relationships in data to simulating policy interventions.

Key Benefits
  • Discover hidden causal relationships from complex data automatically
  • Get AI-recommended identification strategies and control variables
  • Automatically decompose effects into direct and indirect pathways
  • Simulate counterfactual policies before implementation

Projects vs Templates

Understanding the distinction between Projects and Templates is fundamental to using CausalLab effectively.

Projects

Active Work

What: Live research instances with real data

Contains: Your dataset, analysis results, saved progress

Use for: Conducting actual research studies

Templates

Reusable Blueprint

What: Pre-configured research designs

Contains: Methodology, variable definitions, no data

Use for: Starting similar studies quickly

Quick Start Guide

Get started with CausalLab in three simple steps:

1

Choose a Starting Point

Browse pre-built templates in Examples, use your own saved templates, or create a new project from scratch.

2

Upload Your Data

Upload CSV, Excel, or STATA files. Connect to databases like Supabase. Define your treatment, outcome, and control variables.

3

Run Analysis

Progress through the four modules: Causal Discovery → Identification → Mechanism Analysis → Policy Simulation. Export results at any stage.

Causal Discovery Lab

The Causal Discovery Lab uses AI algorithms to automatically identify potential causal relationships in your data, generating visual causal graphs.

Key Features

  • Algorithm Selection: Choose from PC, GES, FCI, or hybrid methods based on your data structure
  • Interactive Graph: Visualize causal relationships with directed edges showing effect strength
  • AI Insights: Get literature comparisons and novel relationship suggestions

Identification Strategy Optimizer

Automatically recommends econometric identification strategies based on your research design and data structure.

Supported Methods

Difference-in-Differences (DID)
Instrumental Variables (IV)
Regression Discontinuity (RDD)
Propensity Score Matching (PSM)

Mechanism Path Analyzer

Decompose total effects into direct and indirect pathways, identifying the mechanisms through which treatment affects outcomes.

Example: Carbon Trading Effect Decomposition

Total Effect-0.45***
Indirect: Green Innovation (42%)-0.19***
Indirect: Energy Structure (31%)-0.14***

Policy Simulation Sandbox

Simulate counterfactual scenarios to predict policy impacts before implementation, with uncertainty quantification.

Simulation Methods

  • Neural ODE for continuous dynamics
  • PySINDy for discovering governing equations
  • Agent-Based Models for heterogeneous agents
  • Monte Carlo for uncertainty quantification

Template-Based Research Workflow

Leverage pre-configured templates to accelerate your research process:

1

Browse Template Library

Explore pre-built templates in /examples or your saved templates in /templates

2

Load Template

Click "Load Template" to create a new project with pre-filled research design

3

Customize & Upload Data

Adjust variables if needed and upload your actual dataset

4

Run Analysis Pipeline

Progress through all four modules with methodology already configured

Exporting Results

Export your analysis results in multiple formats for publication and presentation:

PDF Report

Publication-ready comprehensive report with all tables, figures, and methodology

Excel Data

Raw regression results, effect decomposition, and simulation outputs

STATA/R Code

Reproducible analysis scripts for external validation

Causal Graph

High-resolution causal graph visualization in PNG/SVG format

Need More Help?

Check out our examples, watch tutorial videos, or contact support for personalized assistance.