As a user working with causal analysis, I want to seamlessly discover, visualize, and operationalize causal relationships within my data so that I can make informed, evidence-based decisions and drive impactful outcomes in my field.
- Flexible Data Handling: I can load diverse datasets into Causate, with flexibility to accommodate different input structures and variable features.
- Causal Discovery and Model Selection: I can choose and run a causal discovery model, such as the PC algorithm, and obtain insights that reveal cause-effect relationships in my data.
- Standardized Results and Schema Compatibility: I can retrieve formatted results with a consistent schema, ensuring compatibility with logging and deployment systems.
- Interactive Visualizations: I can generate and explore causal graphs interactively, enabling deeper understanding of relationships, and I can export these visualizations to share with stakeholders.
- Automated Logging for CausalOps: I can log and version models, results, and visualizations to MLflow, allowing me to track my analysis steps, manage model versions, and streamline the deployment process.
- Challenges of Causal Models Solved: I benefit from dynamic signature management, flexible output formatting, and tools that address the unique challenges of causal models, making it possible to integrate causal analysis in a production environment without additional overhead.