causal-ml

Causal Machine Learning in R

Curated resources for causal inference and experimentation

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

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

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

A/B testing meets causal inference on 687k retail customers. Tests whether the arms are actually comparable, corrects the 18% of measured effect that was selection bias, and shows uplift targeting beating response modelling by 67%.

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

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

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

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

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

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.

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'

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

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.

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.