Causal Agent
Platform
A reusable, production-grade AI agent architecture for rigorous causal inference and experimentation.
The Causal Agent Platform is now live for early access testing. Try out the interactive developer portal to run automated causal analyses on your own datasets.
Platform Architecture
Agent Workflow
An iterative tool-calling protocol powered by structured layers:
- Knowledge Retrieval
- Reasoning Traces
- Hybrid Orchestration
- Validation Checks
Advanced Estimators
Unified API for industry-standard causal inference methods:
- Propensity Score Matching
- Double Machine Learning
- Difference-in-differences
- Synthetic Control & BSTS
Production Scaffolding
Built to scale across chat, UI, and internal services:
- API-First (FastAPI)
- Background Jobs Queue
- Typed Artifacts & Caching
- MCP Surface Integration
End-to-End Causal Workflow
The platform explicitly guides the data science lifecycle, guaranteeing methodological rigor from raw data to final estimates.
Inspection & EDA
Automated profiling, missingness checks, outlier detection, and chart suggestions.
Causal Design
Observational and quasi-experimental workflows. Interactive DAG formulation for cross-sectional data.
Rigorous Analysis
Assumption verification, robustness checks, point estimates, and Heterogeneous Treatment Effects (HTE).
Artifacts & Reporting
Executable Python code generation and comprehensive final HTML summary reports.