Course repository for BIOST 578: Causal Inference for Biomedical Studies (Spring 2024) with Dr. Ting Ye

Python implementation of Covariate Balancing Propensity Score (CBPS) for robust causal inference in observational studies. Supports binary, multi-valued, and continuous treatments. Includes high-dimensional CBPS (hdCBPS), nonparametric CBPS (npCBPS), marginal structural models (CBMSM), and instrumental variables (CBIV).(Disclaimer: CURRENTLY WIP)

Zero-to-hero notebooks on causal inference and experimentation for global LLM rollouts: synthetic control, difference-in-differences, propensity scores, regression discontinuity, cluster randomization.

Causal effect of hybrid vs gasoline powertrain on fuel consumption (EPA data, 9 estimators + robustness suite)

Project demonstrating how to implement IPTW for confounding control on complex survey data.

Replication code for Gieng (2026), "Measurement Is Not Passive: Identification of Treatment Effects When Outcomes Are Elicited Rather Than Observed."

Predictimand-Based Mortality Risk Analysis in Ewing Sarcoma: Aligning Prediction with Purpose

A research-grade, 6-week masterclass in Causal Inference and Causal ML from first principles. Rebuilds d-separation oracles, propensity score IRLS engines, doubly-robust AIPW estimators, Cross-Fitting Double Machine Learning (DML), and honest causal forests from scratch in pure NumPy. Fully verified against causal truth

Annotated analysis code for the TRANSFORM-HF Ancillary Biomarker Study: IPW-weighted regression, bootstrap CIs, and causal tree analyses examining biomarker-defined heterogeneity in NT-proBNP response to torsemide vs. furosemide after heart failure hospitalization.