d2cml-ai/CausalAI-Course
Nov 2024 – Dec 2024
2 Months active
Languages Used
JSONJuliaJupyter NotebookMarkdownPython
Technical Skills
Causal InferenceData AnalysisData WranglingDocumentationFile ManagementJupyter Notebooks
Karljaap
PROFILE
Karljaap
Contributed to the d2cml-ai/CausalAI-Course repository by developing a series of machine learning and causal inference artifacts over two months. Delivered comprehensive Jupyter and Julia notebooks that consolidated workflows for causal effect estimation, including Double Lasso, DAGs, and Double/Debiased Machine Learning frameworks. Implemented data preprocessing, feature engineering, and model training using Python and Julia, with techniques such as one-hot encoding, logistic classification, and random forests. Produced detailed technical documentation and reports to enhance reproducibility and support future research. The work emphasized end-to-end data analysis, statistical modeling, and clear report generation, providing reusable resources for decision support and instructional use.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
4
Lines of code
2,109
Activity Months2
Your Network
91 peopleWork History
December 2024
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered an end-to-end Julia notebook for Group 4 Assignment 5 focusing on causal inference, consolidating data loading, ATE calculations, heterogeneous effects via causal forests, Double/Debiased ML coverage, and plotting. Implemented data preprocessing (one-hot encoding) and model training components (Logistic Classifier, Random Forest Regressor). Commit history shows iterative enhancements across five updates to Group4_Assignment_5_Julia.ipynb.
5 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered an end-to-end Julia notebook for Group 4 Assignment 5 focusing on causal inference, consolidating data loading, ATE calculations, heterogeneous effects via causal forests, Double/Debiased ML coverage, and plotting. Implemented data preprocessing (one-hot encoding) and model training components (Logistic Classifier, Random Forest Regressor). Commit history shows iterative enhancements across five updates to Group4_Assignment_5_Julia.ipynb.
December 2024
November 2024
9 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered three major knowledge artifacts and enhanced reproducibility for ML research. Focused on documentation of Airbnb price prediction study, Double Lasso and DAGs notebook work, and documentation of the Double/Debiased ML framework. No major bugs fixed in this period. This set of deliverables provides ready-to-share reports and notebooks for decision support and future research initiatives.
November 2024
9 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered three major knowledge artifacts and enhanced reproducibility for ML research. Focused on documentation of Airbnb price prediction study, Double Lasso and DAGs notebook work, and documentation of the Double/Debiased ML framework. No major bugs fixed in this period. This set of deliverables provides ready-to-share reports and notebooks for decision support and future research initiatives.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture88.6%
Performance80.0%
AI Usage22.8%
Skills & Technologies
Programming Languages
JSONJuliaJupyter NotebookMarkdownPython
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationData WranglingDocumentationFeature EngineeringFile ManagementJupyter NotebooksMachine LearningMachine Learning ConceptsModel TrainingRandom ForestsReport GenerationResearch Analysis
Repositories Contributed To
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