Code for the paper by Falet et al. (2022) "Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning"

This project focuses on Exploratory Data Analysis (EDA) and Uplift Modeling to predict customer response to marketing campaigns.

Developing and Evaluating Causal Inference Methods for Pragmatic Trials

Provides a convenient wrapper function for data analysis with regression discontinuity design (especially discrete running variables) as an identification strategy.

Undersmoothing Causal Estimators with Generative Trees

In this project, we will work on replicating the results of a paper by Imbens & Gelman (2019). The paper states three arguments why high-order polynomials should not be used in regression discontuinity design.

Production-grade causal uplift modeling on 14M rows, benchmarks S-Learner, T-Learner, and FT-Transformer challengers on the Criteo dataset, with Optuna tuning, MLflow tracking, FastAPI + Docker + Google Cloud Run serving, and a Streamlit dashboard.

This project potray's my capstone work. My work involved cleaning, manipulation and analyzing huge data sets and implementing ML models for predictive analysis. This analysis demonstrates that data-driven credit interventions can meaningfully reduce financial exposure while balancing customer retention.