Reliable and Fair Causal Machine Learning for Sparse Subpopulations in NSDUH 2021–2023
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Updated
Nov 26, 2025 - Jupyter Notebook
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.
Production-grade 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.
Code and data for my article 'The Economist's Guide to Causal Forests'
Causal ML for drug discovery: treatment effect estimation, causal graphs, propensity score methods, and perturbation response prediction.
Causal-AIRL: MSc research code + interactive demo. 23pp↑ cross-style policy agreement via latent Z deconfounding. MSc Data Science @ Edinburgh 2024-25.
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