causal-ml

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

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

Reproducible benchmark of uplift-modeling approaches (meta-learners, causal forests, DML, IV) on the Criteo Uplift dataset.

A-ICF: Auditing, Not Predicting — A Causal Bias-Decomposition Framework for Clinical Fairness. Code, Figures, and Tables for OMLET 2026 (Paper ID: 596).

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.

Динамическое ценообразование на графе: Dijkstra OD, additive surge, switchback A/B; опциональный fail-open uplift через Causal Pricing Engine

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

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.

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

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

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

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.

Judea Pearl’s Causal Ladder, featuring Association, Intervention, and Counterfactual models.

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

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