treatment-effect-estimation

[ NeurIPS 2023 ] Official Codebase for "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects"

Code for TEDVAE, a VAE-based treatment effect estimation algorithm.

AAAI 22 - Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Causal weights for macroeconomic shocks

The official code of "Adversarial Counterfactual Environment Model Learning" (NeurIPS'23 spotlight)

Implementation for the paper "Detecting critical treatment effect bias in small subgroups"

Prior-fitted networks for time-series causal inference and longitudinal counterfactual outcome prediction.

Official code for "Resolving the bias-precision paradox in medical AI to reduce risks in decision support". GITO is a causal inference framework for treatment outcome prediction in critical care, featuring sampling-based MMD (sMMD) for distributional alignment, Integrated Gradients interpretability, and LLM-powered clinical narratives.

[ AISTATS 2026 ] Official Codebase for "Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects"

Applying AI to medical use cases: Diagnoses of lung and brain disorders, Building risk models and survival estimators for heart disease via RF, and Using NLP to extract information from radiology reports.

Code for 'Estimating Treatment Effects with Independent Component Analysis' (arXiv:2507.16467)

Causal uplift modelling framework

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