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

The concept of using a LLM for developing a work plan.

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

This repository aims to explore all possibilities available on Microsoft's DoWhy package, based on the Causal Inference Theory and Principles.

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 Inference for Marketplace

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

Internship Project on Causal Inference (The causal effect of multi-level treatment of intervention using observational data).

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

Causal discovery pipeline for Bitcoin return drivers — PC Algorithm, NOTEARS, PCMCI, Granger + DoWhy falsification. In partnership with ESILV and Ginjer AM.

End-to-end customer churn prediction using PyTorch, XGBoost, SHAP, Lime, Optuna & MLflow — with business ROI analysis and FastAPI serving.

MUMPS-max1D-or-2D

Causal inference on bank marketing data — PSM, DoWhy, and EconML Causal Forest to estimate true effect of cellular contact on subscription

propensity score matching with DoWhy

PERT-max1D-or-3D

IISc/CSA E0-294: Systems for Machine learning - Course project on employing causal insights in DNN model pruning and performance

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

An explainable and causal analysis of recruitment decision pipelines using machine learning, SHAP, and causal inference.

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

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