The cause2e package provides tools for performing an end-to-end causal analysis of your data. Developed by Daniel Grünbaum (@dg46).

DoWhy/EconML toolkit for visualizing causal paths and estimating treatment effects

A Streamlit web application for discovering causal relationships in your data using Microsoft's DoWhy library. This tool helps you identify and quantify causal effects between variables in your datasets through correlation-based graph discovery and rigorous causal inference.

Causal reasoning middleware for LLMs — catches false causal claims in AI outputs

Employee performance analytics with 9-box grid, clustering, causal inference (DoWhy), SHAP explainability, and ML prediction. FastAPI + Streamlit.

An agentic causal inference framework that discovers business drivers, monitors for 'causal drift,' and autonomously recalibrates models using Claude Code + Ralph Loop.

DoWhy Streamlit app focusing on Causal Inference

Causal inference analysis of ICU beta-blocker treatment effects using propensity matching, IPW, doubly robust estimation, Double ML, and Causal Forest on eICU data

Causal inference for infrastructure root cause analysis

Causal inference pipeline — propensity matching, doubly-robust estimation, uplift modeling, A/B test analysis with DoWhy + EconML.

Causal inference project using DoWhy to isolate the true marketing lift of bank contact methods. Applies Propensity Score Stratification to remove selection bias from raw campaign data and delivers an interactive ROI simulator for budget decision-making.

AI-powered medical chatbot with Causal AI + RAG for symptom-based disease diagnosis