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
The repository provides state-of-arts machine-learning approaches to revamping firm fixed effects models in finance studies.
This library provides packages on DoubleML / Causal Machine Learning and Neural Networks in Python for Simulation and Case 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.
2023학년도 2학기 경기변동론 프로젝트 페이지
Final project for ECON434
The repository is for the publication at: Duong K (2024) What really matters for global intergenerational mobility? PLoS ONE 19(6): e0302173.
Comparing effectiveness of the most common causal machine learning methods across various treatment effect, model complexities, data dimensions and sample sizes.
cause /kôz/ noun 1. a person or thing that gives rise to an action, phenomenon, or condition.
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