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 five-layer causal-neuro-symbolic framework for machine fault diagnosis. Independently verifies neural predictions against machine physics; domain-agnostic via pluggable providers.

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 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).

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

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.

Prescriptive churn analytics with calibrated risk, uplift evidence, SHAP, counterfactuals, expected-value decisions, and a Next.js operations dashboard.

End-to-end Causal ML & measurement framework for clinical decisions: DoWhy + EconML Double ML estimate the true effect of early vasopressor timing on ICU mortality (MIMIC-IV), with a pre-registered GO/NO-GO governance gate, E-value sensitivity, refutation tests, auto model card/PDF reports, and a Streamlit CATE explorer.

propensity score matching with DoWhy

PERT-max1D-or-3D

MUMPS-max1D-or-2D

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

End-to-end campaign attribution pipeline: Airflow + dbt + BigQuery over 4.3M GA4 events, DoWhy causal inference separating true paid-traffic lift from selection bias, governed metrics via MetricFlow, and a Looker Studio dashboard.

Bayesian Causal Inference in Doubly Gaussian DAG-probit Models

🌍 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.