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.
Causal Inference for Marketplace
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.
Internship Project on Causal Inference (The causal effect of multi-level treatment of intervention using observational data).
IISc/CSA E0-294: Systems for Machine learning - Course project on employing causal insights in DNN model pruning and performance
An agentic causal inference framework that discovers business drivers, monitors for 'causal drift,' and autonomously recalibrates models using Claude Code + Ralph Loop.
propensity score matching with DoWhy
10 AI agents × 4 layers × LangGraph state machine orchestrator. OR-Tools CP-SAT MILP optimization, DoWhy causal inference, LSTM+XGBoost+Chronos-2 forecasting, CircuitBreaker resilience, AgentTracer observability, AWS backend (S3/Redshift/Athena/QuickSight), 120+ tests. Zero API keys needed.
Beauty & skincare supply chain AI platform. Social signal-aware demand forecasting (MAPE 12%), CUPED A/B testing (55% variance reduction), X-Learner uplift (AUUC 0.74), DoWhy causal inference, 5 SQL analytics pipelines, MLflow + Evidently MLOps, SHAP/LIME explainability, 120+ tests.
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.
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models
estimate CATE and deploy uplift‑based targeting models that replace or augment traditional A/B testing.
Causal inference for infrastructure root cause analysis
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