d2cml-ai/CausalAI-Course
Nov 2024 – Dec 2024
2 Months active
Languages Used
JuliaJupyter NotebookMarkdownPythonHTMLShell
Technical Skills
Assignment InitializationBootstrap SamplingData AnalysisData PreprocessingData ScienceDecision Trees
LizNagh
PROFILE
Liznagh
Contributed to the d2cml-ai/CausalAI-Course repository by developing and enhancing machine learning and causal inference coursework materials over a two-month period. Built and updated Jupyter and Julia notebooks to scaffold assignments, document methodologies such as Double/Debiased Machine Learning, and implement end-to-end workflows for estimating treatment effects. Focused on reproducibility and onboarding by improving data loading, preprocessing, and model training pipelines using Python and Julia. Performed repository cleanup and restructuring to streamline maintenance and improve clarity for students and instructors. Emphasized technical writing and documentation to support learning outcomes, while reinforcing collaborative research and code management practices throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
6
Lines of code
9,746
Activity Months2
Your Network
91 peopleWork History
December 2024
6 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered a fully functional Assignment 5 notebook implementing causal inference with Double Machine Learning (ATE) and completed repository cleanup to streamline maintenance. The work improves course quality, reliability, and onboarding efficiency for students and instructors by providing a clear, end-to-end DML workflow and a leaner project structure.
6 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered a fully functional Assignment 5 notebook implementing causal inference with Double Machine Learning (ATE) and completed repository cleanup to streamline maintenance. The work improves course quality, reliability, and onboarding efficiency for students and instructors by providing a clear, end-to-end DML workflow and a leaner project structure.
December 2024
November 2024
7 Commits • 4 Features
Nov 1, 2024November 2024: Key features delivered across d2cml-ai/CausalAI-Course focused on documentation and coursework scaffolds. Implemented Report 4 documentation init and content update for Airbnb price prediction article methodology; established Julia-based Group 4 Assignment 3/4 notebook scaffolds; upgraded ML/DS notebooks for data loading, preprocessing, and model training (including Decision Tree and bootstrap CI); initiated Report 5 notebook documenting Double/Debiased ML methods. No major bugs fixed this month; contributions improved reproducibility, onboarding, and learning outcomes, while reinforcing research workflows and collaboration patterns. Technologies demonstrated include Markdown/doc tooling, Jupyter/Julia notebooks, data preprocessing pipelines, ML modeling, and Git-based version control.
November 2024
7 Commits • 4 Features
Nov 1, 2024November 2024: Key features delivered across d2cml-ai/CausalAI-Course focused on documentation and coursework scaffolds. Implemented Report 4 documentation init and content update for Airbnb price prediction article methodology; established Julia-based Group 4 Assignment 3/4 notebook scaffolds; upgraded ML/DS notebooks for data loading, preprocessing, and model training (including Decision Tree and bootstrap CI); initiated Report 5 notebook documenting Double/Debiased ML methods. No major bugs fixed this month; contributions improved reproducibility, onboarding, and learning outcomes, while reinforcing research workflows and collaboration patterns. Technologies demonstrated include Markdown/doc tooling, Jupyter/Julia notebooks, data preprocessing pipelines, ML modeling, and Git-based version control.
Activity
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Quality Metrics
Correctness87.6%
Maintainability89.2%
Architecture87.6%
Performance81.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
HTMLJuliaJupyter NotebookMarkdownPythonShell
Technical Skills
Assignment InitializationBootstrap SamplingCausal InferenceCode CleanupData AnalysisData CleaningData LoadingData PreprocessingData ScienceDecision TreesJuliaJulia ProgrammingJupyter NotebooksMachine LearningModel Training
Repositories Contributed To
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