DoubleML - Double Machine Learning in Python

DoubleML - Double Machine Learning in R

Taking causal inference to the extreme!

Sensitivity analysis tools for causal ML

DoubleML-Serverless - Distributed Double Machine Learning with a Serverless Architecture

Coverage Simulations for DoubleML package

This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case Studies.

The repository provides state-of-arts machine-learning approaches to revamping firm fixed effects models in finance studies.

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.

Master's degree thesis project using Debiased Machine Learning to estimate treatment effects from economic policy in US funds performance.

Causal Machine Learning project analyzing and evaluating different Double ML models for estimating treatment effects in observational data.

Compare ATE estimates with different DML approaches

Python code to estimate ATE with Doubly Robust method

2023학년도 2학기 경기변동론 프로젝트 페이지

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.

The repository is for the publication at: Duong K (2024) What really matters for global intergenerational mobility? PLoS ONE 19(6): e0302173.

Final project for ECON434

cause /kôz/ noun 1. a person or thing that gives rise to an action, phenomenon, or condition.

Comparing effectiveness of the most common causal machine learning methods across various treatment effect, model complexities, data dimensions and sample sizes.

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.