d2cml-ai/CausalAI-Course
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSVJuliaMarkdownPythonR
Technical Skills
BaggingCausal InferenceData AnalysisData EngineeringData ManagementData Science
Rodrigo A. Grijalba
PROFILE
Rodrigo A. Grijalba
Developed educational content for the d2cml-ai/CausalAI-Course repository, focusing on cross-language causal inference workflows. Delivered end-to-end Causal Forests and Random Forests notebooks using R and Python, leveraging the NSW dataset to demonstrate practical causal analysis. Enhanced Julia notebooks with Double Machine Learning via cross-fitting and introduced a Mixed Model regression workflow integrating RandomForestRegressor, LassoCVRegressor, and FixedEffectModels. Prioritized notebook reproducibility and stability across R, Python, and Julia environments, refining compatibility and documentation to support onboarding and teaching. Applied skills in data science, econometrics, and machine learning to improve curriculum quality and provide hands-on exposure to advanced statistical modeling techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
22,183
Activity Months1
Your Network
24 peopleWork History
November 2024
7 Commits • 2 Features
Nov 1, 2024November 2024 (2024-11): Delivered cross-language causal inference education content in CausalAI-Course and strengthened notebook reproducibility across R, Python, and Julia. Key features include end-to-end Causal Forests and Random Forests notebooks with NSW causal inference datasets (R and Python) and substantial Julia notebook enhancements for Double Machine Learning (cross-fitting) and a Mixed Model regression workflow using RandomForestRegressor, LassoCVRegressor, and FixedEffectModels integration. Minor stability and compatibility refinements across notebooks were implemented, with no high-severity bugs reported. These updates improve learner onboarding, curriculum quality, and practical exposure to state-of-the-art causal inference methods on real-world data, delivering measurable business value in education-focused content.
7 Commits • 2 Features
Nov 1, 2024November 2024 (2024-11): Delivered cross-language causal inference education content in CausalAI-Course and strengthened notebook reproducibility across R, Python, and Julia. Key features include end-to-end Causal Forests and Random Forests notebooks with NSW causal inference datasets (R and Python) and substantial Julia notebook enhancements for Double Machine Learning (cross-fitting) and a Mixed Model regression workflow using RandomForestRegressor, LassoCVRegressor, and FixedEffectModels integration. Minor stability and compatibility refinements across notebooks were implemented, with no high-severity bugs reported. These updates improve learner onboarding, curriculum quality, and practical exposure to state-of-the-art causal inference methods on real-world data, delivering measurable business value in education-focused content.
November 2024
Activity
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Quality Metrics
Correctness85.8%
Maintainability85.8%
Architecture85.8%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJuliaMarkdownPythonR
Technical Skills
BaggingCausal InferenceData AnalysisData EngineeringData ManagementData ScienceEconMLEconometricsEducational Content CreationJulia ProgrammingJupyter NotebooksMachine LearningMatplotlibNumPyPandas
Repositories Contributed To
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