Causal Wizard offers a no-code web platform for subject-matter experts to model causal relationships and derive quantitative insights from observational data. It enables users to predict intervention outcomes and explore counterfactual scenarios by integrating domain knowledge into causal diagrams and leveraging causal inference techniques.
Funding
Funding not disclosed
Founders
Product
Problem
Establishing and quantifying cause-and-effect relationships from observational data is challenging, as standard machine learning models often conflate correlation with causation. This can lead to inaccurate predictions of intervention outcomes and a misunderstanding of counterfactual scenarios.
Solution
Causal Wizard provides a web-based platform that empowers subject-matter experts to model causal relationships using causal inference and machine learning techniques. The application enables users to derive quantitative causal insights directly from their existing data without requiring programming expertise or advanced mathematical knowledge. By leveraging causal models, users can accurately predict the impact of interventions and explore hypothetical "what-if" scenarios. The platform facilitates the integration of domain knowledge to build robust causal diagrams, which are then used to analyze data and estimate causal effects.
Target Audience
The platform is designed for subject-matter experts, including product managers, asset managers, scientists, and engineers, who possess deep knowledge of the systems they are studying and need to understand causal relationships within their data.
Features
- Web-app interface for causal model specification and data analysis.
- Support for building causal diagrams to encode expert domain knowledge.
- Integration of causal inference libraries, including DoWhy and DoubleML.
- Ability to analyze observational data to estimate causal effects.
- Functionality to predict intervention outcomes and analyze counterfactuals.
- No-code interface designed for users without programming or advanced math backgrounds.
- Underlying implementation leverages Python libraries such as NumPy and Pandas for data manipulation and computation.
- Visualization tools for causal diagrams and analysis results.