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).
Prescriptive churn analytics with calibrated risk, uplift evidence, SHAP, counterfactuals, expected-value decisions, and a Next.js operations dashboard.
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.
A guardrail layer for causal estimates: separates identification and refutation (DoWhy) from estimation (statsmodels) and runs refutation tests before an effect size is trusted.
propensity score matching with DoWhy
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.
Causal inference on bank marketing data — PSM, DoWhy, and EconML Causal Forest to estimate true effect of cellular contact on subscription
Production-grade causal inference service for marketing uplift with honest tiered findings
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