d2cml-ai/CausalAI-Course
Nov 2024 – Dec 2024
2 Months active
Languages Used
DOTJuliaJupyter NotebookMarkdownPythonSQLHTMLJSON
Technical Skills
Academic SummarizationBootstrappingCausal InferenceData AnalysisData PreprocessingData Visualization
almendraval10
PROFILE
Almendraval10
Contributed to the d2cml-ai/CausalAI-Course repository by developing and refining educational resources and research documentation focused on causal inference. Built end-to-end Jupyter notebooks in Python and Julia for analyzing heterogeneous treatment effects, including data loading, preprocessing, modeling with causal trees and forests, and visualizing feature importances. Enhanced reproducibility and cross-language consistency by consolidating notebooks and maintaining repository hygiene. Addressed notebook execution integrity by fixing file paths and execution counts, ensuring reliable workflows for teaching and research. Leveraged skills in data wrangling, statistical modeling, and technical writing to support data-driven decision-making and facilitate onboarding for data scientists and researchers.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
22Total
Bugs
2
Commits
22
Features
3
Lines of code
23,625
Activity Months2
Your Network
91 peopleWork History
December 2024
16 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered an end-to-end heterogeneous treatment effects notebook suite across Python and Julia, enabling data loading, preprocessing, modeling (ATE estimation via causal trees/forests), and visualization of feature importances and predicted treatment effects. Consolidated a cross-language workflow with a final version across notebooks and ensured reproducibility. Performed targeted maintenance by removing a deprecated notebook to reduce confusion and keep materials relevant to causal inference workflows. Impact: empowers data scientists to analyze heterogeneous causal effects to inform intervention decisions, improves decision-making support, and enhances onboarding and reproducibility. Technologies/skills demonstrated: Python and Julia notebook development, causal inference modeling (trees/forests), data visualization, data wrangling, cross-language integration, and Git/version-control discipline.
16 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for d2cml-ai/CausalAI-Course: Delivered an end-to-end heterogeneous treatment effects notebook suite across Python and Julia, enabling data loading, preprocessing, modeling (ATE estimation via causal trees/forests), and visualization of feature importances and predicted treatment effects. Consolidated a cross-language workflow with a final version across notebooks and ensured reproducibility. Performed targeted maintenance by removing a deprecated notebook to reduce confusion and keep materials relevant to causal inference workflows. Impact: empowers data scientists to analyze heterogeneous causal effects to inform intervention decisions, improves decision-making support, and enhances onboarding and reproducibility. Technologies/skills demonstrated: Python and Julia notebook development, causal inference modeling (trees/forests), data visualization, data wrangling, cross-language integration, and Git/version-control discipline.
December 2024
November 2024
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for d2cml-ai/CausalAI-Course focusing on delivering causal inference resources, improving notebook reliability, and producing research-oriented documentation. The work enhances teaching and research capabilities in causal analysis for wage studies and youth smoking, with concrete material ready for deployment and evaluation.
November 2024
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for d2cml-ai/CausalAI-Course focusing on delivering causal inference resources, improving notebook reliability, and producing research-oriented documentation. The work enhances teaching and research capabilities in causal analysis for wage studies and youth smoking, with concrete material ready for deployment and evaluation.
Activity
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Quality Metrics
Correctness80.8%
Maintainability80.8%
Architecture80.8%
Performance78.0%
AI Usage21.8%
Skills & Technologies
Programming Languages
DOTHTMLJSONJuliaJupyter NotebookMarkdownPythonSQLSVG
Technical Skills
Academic SummarizationBootstrappingCausal InferenceData AnalysisData PreprocessingData VisualizationData WranglingDecision TreesDecisionTreeDirected Acyclic Graphs (DAGs)DocumentationEconMLFeature EngineeringJuliaJupyter Notebook
Repositories Contributed To
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