d2cml-ai/CausalAI-Course
Nov 2024 – Dec 2024
2 Months active
Languages Used
Plain TextR
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationEconometricsMachine Learning
ja-u6549
PROFILE
Ja-u6549
Worked on the d2cml-ai/CausalAI-Course repository, developing end-to-end tooling for causal inference and heterogeneous treatment effect analysis in R. Built a Causal Inference Analysis Toolkit that supports data loading, preprocessing, Lasso regression with cross-validation, OLS comparison, and Directed Acyclic Graph visualizations to clarify causal relationships. Authored a Double/Debiased Machine Learning methodology report, detailing theoretical and computational aspects for robust estimation. Later, implemented heterogeneous treatment effects analysis using causal trees, forests, and Double Machine Learning, enabling group comparisons across demographic and economic segments. Maintained code quality and reproducibility throughout, leveraging skills in R programming, data analysis, and statistical modeling.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
1,211
Activity Months2
Your Network
91 peopleWork History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for the developer work on the CausalAI-Course project. Delivered an end-to-end Heterogeneous Treatment Effects Analysis capability using Causal Trees/Forests (R) and Double Machine Learning (DML). Implemented data loading, model fitting, and visualization of predicted effects across demographic and economic groups, including group comparisons and robust estimation via DML. The feature is implemented in d2cml-ai/CausalAI-Course with a dedicated commit that ties to the R-based analysis work.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for the developer work on the CausalAI-Course project. Delivered an end-to-end Heterogeneous Treatment Effects Analysis capability using Causal Trees/Forests (R) and Double Machine Learning (DML). Implemented data loading, model fitting, and visualization of predicted effects across demographic and economic groups, including group comparisons and robust estimation via DML. The feature is implemented in d2cml-ai/CausalAI-Course with a dedicated commit that ties to the R-based analysis work.
December 2024
November 2024
2 Commits • 2 Features
Nov 1, 2024Month 2024-11 highlights two key feature workstreams for the CausalAI-Course repository, focused on practical tooling and rigorous methodology in causal inference. Delivered an R-based Causal Inference Analysis Toolkit with end-to-end capabilities (data loading, preprocessing, Lasso regression with cross-validation, and OLS comparison) plus DAG visualizations to illustrate causal relationships. Published a comprehensive Double/Debiased Machine Learning (DML) methodology report, outlining theoretical foundations, handling of high-dimensional nuisance parameters, practical applicability, and computational considerations. No major bugs reported; maintained code quality and reproducibility across the repository. These efforts collectively enhance instructional tooling, enable reproducible experiments, and provide scalable analytical methods with clear business value for learning outcomes and future research deployment.
November 2024
2 Commits • 2 Features
Nov 1, 2024Month 2024-11 highlights two key feature workstreams for the CausalAI-Course repository, focused on practical tooling and rigorous methodology in causal inference. Delivered an R-based Causal Inference Analysis Toolkit with end-to-end capabilities (data loading, preprocessing, Lasso regression with cross-validation, and OLS comparison) plus DAG visualizations to illustrate causal relationships. Published a comprehensive Double/Debiased Machine Learning (DML) methodology report, outlining theoretical foundations, handling of high-dimensional nuisance parameters, practical applicability, and computational considerations. No major bugs reported; maintained code quality and reproducibility across the repository. These efforts collectively enhance instructional tooling, enable reproducible experiments, and provide scalable analytical methods with clear business value for learning outcomes and future research deployment.
Activity
Loading activity data...
Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance66.6%
AI Usage26.6%
Skills & Technologies
Programming Languages
Plain TextR
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationEconometricsMachine LearningR ProgrammingStatistical InferenceStatistical Modeling
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline