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.
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.
Employee performance analytics with 9-box grid, clustering, causal inference (DoWhy), SHAP explainability, and ML prediction. FastAPI + Streamlit.
Causal Inference for Marketplace
Causal discovery pipeline for Bitcoin return drivers — PC Algorithm, NOTEARS, PCMCI, Granger + DoWhy falsification. In partnership with ESILV and Ginjer AM.
Internship Project on Causal Inference (The causal effect of multi-level treatment of intervention using observational data).
Causal reasoning middleware for LLMs — catches false causal claims in AI outputs
Causal impact of retail coupon campaigns: ATE/CATE estimates, uplift curves, DoWhy checks, Streamlit app
Estimates whether an intervention actually caused an outcome, from observational data: propensity matching, IPW, S/T/X-learners, DiD and IV. Then tries to break its own result with refutation tests and an E-value — and reports "no effect" when that is the honest answer.
End-to-end causal inference study estimating the effect of smoking cessation on substantial weight gain using propensity methods, DoWhy, and doubly robust EconML estimators.
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.
Causal analysis for people who aren't data scientists: an Agent Skill for Claude
Comprehensive causal inference framework combining rigorous statistical foundations (MSc-level) with practical ML integration — DoWhy, EconML, DAGs, counterfactuals, A/B testing
Applied Causal Inference framework: DAGs, d-separation, Structural Causal Models, and Meta-Learners (S/T/X) to isolate true policy treatment effects beyond passive correlation.
🌍 MCP server giving Claude live environmental data: air quality, wildfires, and weather, plus statistical anomaly detection and causal inference (DoWhy) to explain why pollution spikes happen.
To associate your repository with the dowhy topic, visit your repo's landing page and select "manage topics."