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
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.
propensity score matching with DoWhy
This project is a comprehensive data science initiative focused on improving the lives of animals in shelters. We aim to leverage advanced analytical methods to predict shelter animal outcomes and optimize resource allocation.
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.
An agentic causal inference framework that discovers business drivers, monitors for 'causal drift,' and autonomously recalibrates models using Claude Code + Ralph Loop.
Causal inference study of ESG ratings and institutional capital flows. Python + DoWhy + stacked difference-in-differences.
estimate CATE and deploy uplift‑based targeting models that replace or augment traditional A/B testing.
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