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

Causal Machine Learning in R

Curated resources for causal inference and experimentation

AI-powered marketing attribution: multi-touch attribution with causal ML, media mix modeling with Robyn/LightweightMMM, incrementality testing and marketing ROI optimization dashboard

End-to-end MLOps pipeline for online causal inference: DoWhy/EconML DML models trained on Databricks, served on Azure Kubernetes via CI/CD.

Atribuição de conversão multi-touch com ML causal. Revela a real contribuição de cada ponto de contato na jornada do cliente.

Code for "Language Models as Causal Effect Generators" (SD-SCMs) — adds GB10/DGX Spark unified memory support

Causal Inference Engine using T-Learners (XGBoost) to optimize marketing ROI. Features: 3.2x Lift over random targeting, Behavior-Based Segmentation (Persuadables vs. Sleeping Dogs), and fully dockerized FastAPI/Streamlit architecture.

Causal ML for drug discovery: treatment effect estimation, causal graphs, propensity score methods, and perturbation response prediction.

"Causal Machine Learning for Cost-Effective Allocation of Electricity Aid" thesis for my Masters in Management and Digital Technologies at Ludwig-Maximillian Univeristy, Munich.

Causal-AIRL: MSc research code + interactive demo. 23pp↑ cross-style policy agreement via latent Z deconfounding. MSc Data Science @ Edinburgh 2024-25.

Code and data for my article 'The Economist's Guide to Causal Forests'

Customer churn prediction with explainable AI: gradient boosting + SHAP explanations, causal ML for intervention recommendations, real-time scoring API and retention campaign automation

Chapter wise source code for Causal Machine Learning book by Durai Rajamanickam

Reliable and Fair Causal Machine Learning for Sparse Subpopulations in NSDUH 2021–2023

Causal bandit orchestration platform for real-time adaptive experimentation, sequential testing, and interference-aware decisioning.

CANS: Production-ready causal inference with GNNs, Transformers, CFRNet and LLM integration. The most comprehensive causal AI framework.

OLS on observational data says job training hurts earnings. Double ML corrects the bias and recovers the $1,794 RCT ground truth. Per-individual CATE · SHAP moderators · FastAPI · Streamlit dashboard.

Causal ML for business decisions: DoWhy + EconML for causal inference, uplift modeling, treatment effect estimation and counterfactual analysis for marketing/pricing/product experiments

Causal analysis framework using Double Machine Learning to quantitatively isolate the effect of model size on deep learning performance while controlling for confounders such as dataset size, training time, and hyperparameters.