Causal AI for Reliable Decision-Making
From messy data to robust findings in minutes
Why Causal AI
Scientific discovery and reliable decision-making require causal reasoning, not just correlations.
Most data analysis stops at finding patterns, leaving critical questions unanswered. Whether you're evaluating a treatment, optimizing a process, or trying to understand a pehnomenon, you need to know what actually causes what. Causal inference provides the tools to answer these questions rigorously.
However, applying causal inference in practice requires deep technical expertise, domain-specific knowledge, and careful tracking and validation of results. This creates a significant barrier for many researchers and analysts who need to extract reliable insights from their data but lack the resources or expertise to do so effectively.
We make cutting-edge causal inference tools accessible for analysts, scientists, and clinicians.
A Platform for End-to-End Causal Analysis
Interactive Guided Analysis
Specialzed Agents that guides you through every step of causal analysis using natural language. No coding required - just choose your workflow, upload your data, and our system walks you through the entire process.
- Natural language conversations - no technical jargon required
- Personalized analysis paths adapted to your specific data and questions
- Real-time guidance and explanations at every decision point
- Built-in best practices and quality checks throughout the process
Comprehensive Toolkit
Our platform integrates the latest research in causal inference, machine learning, and statistical methods, ensuring your analysis meets the highest academic and industry standards.
- Complete toolkit for all causal analysis needs - effect estimation, model discovery, and causal prediction
- From traditional econometrics to modern machine learning approaches.
- Intelligent method recommendation based on your data and specific research question
A Comprehensive Library of Causal Inference Methods
Built-in Validation & Robustness Checks
Rigorous scientific validation built into every analysis. Our agents runs multiple robustness checks, sensitivity analyses, and refutation tests to ensure your conclusions are reliable and defensible.
- Comprehensive battery of validation tests for each causal claim
- Automated sensitivity analysis across key assumptions
- Statistical significance testing with multiple comparison corrections
- Clear reporting of limitations, confidence intervals, and potential biases
Validation Test Results
Automated robustness checks for causal claims
Results are robust and reliable
Workflows Tailored to Your Research Questions
Choose from an expanding set of workflows each designed to answer specific causal questions with buiilt-in best practices and domain expertise.
Measure the impact of interventions, or policies on your outcomes of interest.
Understand the underlying causes of certain behaviors or outcomes in your data.
Predict what would happen under different scenarios and optimize interventions.
Get in Touch
Have a question or want to see Zatoona in action? Send us a message and we'll get back to you.