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double-machine-learning

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Cusal Inference applied to timeseries, uses an event database to generate a timeseries of the outcome given a sliding window containing events. Useful to add causal outcomes of events into multivariate timeseries forecasting models.

  • Updated Jan 22, 2025
  • Python

Causal analysis of Uber's impact on public transit ridership using double machine learning. Found 4.28% increase in ridership, suggesting complementary relationship. Advanced econometric methods with 76K+ observations.

  • Updated Jul 9, 2025
  • Jupyter Notebook

A comprehensive causal inference analysis investigating whether HbA1c testing during hospitalization reduces 30-day readmission rates for diabetic patients. This project employs Double Machine Learning (DoubleML) with multiple ML algorithms (Random Forest, XGBoost, Decision Trees, Logistic Regression) to estimate causal effects.

  • Updated Jul 27, 2025
  • Python

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