d2cml-ai/CausalAI-Course
Aug 2025 – Dec 2025
4 Months active
Languages Used
CSSCSVHTMLJuliaMarkdownPythonRShell
Technical Skills
BootstrapCausal InferenceData AnalysisData EngineeringData IngestionData Processing
albertotrelles
PROFILE
Albertotrelles
Worked on the d2cml-ai/CausalAI-Course repository, delivering ten features over four months to enhance course infrastructure, reproducibility, and practical experimentation for causal inference education. Developed web scaffolding, organized curriculum materials, and expanded datasets to support hands-on learning. Implemented advanced machine learning workflows, including RLasso and Double Lasso regression, neural network-based double machine learning, and Difference-in-Differences estimators, using Python, Julia, and R. Created reproducible Jupyter notebooks with cross-validation and robust data engineering practices. Contributed educational content aligned with course objectives, emphasizing maintainability and accessibility. The work improved research tooling, accelerated policy-relevant analysis, and strengthened the course’s technical foundation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
10
Lines of code
6,527,959
Activity Months4
Your Network
24 peopleWork History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Lecture 4 Educational Content for d2cml-ai/CausalAI-Course, adding curriculum-aligned materials and practical examples. This work enhances course completeness and learner value. No major bugs fixed this month; the focus was content delivery and repository readiness. Business impact includes improved course quality and readiness for next module; Skills demonstrated include content design, version control, and curriculum alignment.
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Lecture 4 Educational Content for d2cml-ai/CausalAI-Course, adding curriculum-aligned materials and practical examples. This work enhances course completeness and learner value. No major bugs fixed this month; the focus was content delivery and repository readiness. Business impact includes improved course quality and readiness for next module; Skills demonstrated include content design, version control, and curriculum alignment.
December 2025
November 2025
6 Commits • 3 Features
Nov 1, 2025November 2025 — d2cml-ai/CausalAI-Course: Delivered end-to-end causal inference enhancements and reproducible research tooling. Key features include a neural network-based double machine learning (NN-DML) framework for causal effect estimation (gun ownership on homicide rates) with cross-validation and multiple regressors; a comprehensive Difference-in-Differences (DiD) suite including simulations, ATT estimation, and staggered adoption estimators (Callaway & Sant'Anna); and a Debiased Machine Learning implementation for partially linear regression in Julia, with a new notebook and data loading setup. Notable commits across the month: NN_DML work (b70181e7..., 7adab709...), DiD suite (c3b87ff2..., 5664c5f5..., 8da8060e1...), Julia Debiased ML (ff8fd401...). Notebook updates to improve reproducibility (Update julia.ipynb). Overall impact: expanded causal inference capabilities, cross-language research tooling, and ready-to-run experiments that accelerate policy-relevant analyses. Technologies demonstrated: Python, R, Julia; Jupyter notebooks; DML, DiD, ATT estimators; cross-validation; debiased ML; reproducible research practices. Business value: enables robust, policy-relevant causal analysis; reduces time to insights; improves methodological rigor and accessibility for researchers and stakeholders.
November 2025
6 Commits • 3 Features
Nov 1, 2025November 2025 — d2cml-ai/CausalAI-Course: Delivered end-to-end causal inference enhancements and reproducible research tooling. Key features include a neural network-based double machine learning (NN-DML) framework for causal effect estimation (gun ownership on homicide rates) with cross-validation and multiple regressors; a comprehensive Difference-in-Differences (DiD) suite including simulations, ATT estimation, and staggered adoption estimators (Callaway & Sant'Anna); and a Debiased Machine Learning implementation for partially linear regression in Julia, with a new notebook and data loading setup. Notable commits across the month: NN_DML work (b70181e7..., 7adab709...), DiD suite (c3b87ff2..., 5664c5f5..., 8da8060e1...), Julia Debiased ML (ff8fd401...). Notebook updates to improve reproducibility (Update julia.ipynb). Overall impact: expanded causal inference capabilities, cross-language research tooling, and ready-to-run experiments that accelerate policy-relevant analyses. Technologies demonstrated: Python, R, Julia; Jupyter notebooks; DML, DiD, ATT estimators; cross-validation; debiased ML; reproducible research practices. Business value: enables robust, policy-relevant causal analysis; reduces time to insights; improves methodological rigor and accessibility for researchers and stakeholders.
September 2025
2 Commits • 2 Features
Sep 1, 2025September 2025: Delivered two feature enhancements in d2cml-ai/CausalAI-Course that advance practical causal inference workflows for learners and researchers. Implemented RLasso-based regression workflow in the enhanced data science notebook using hdmpy, including an RLasso class, Lasso cross-validation, and improvements to reproducibility through updated notebook counts and formatting. Added a wage-gap analysis notebook exploring heterogenous effects by sex with Double Lasso, featuring data loading, feature engineering, and a Double Lasso regression implementation to study socio-economic interactions. These updates improve teaching relevance, reproducibility, and the ability to derive robust insights from real-world data.
2 Commits • 2 Features
Sep 1, 2025September 2025: Delivered two feature enhancements in d2cml-ai/CausalAI-Course that advance practical causal inference workflows for learners and researchers. Implemented RLasso-based regression workflow in the enhanced data science notebook using hdmpy, including an RLasso class, Lasso cross-validation, and improvements to reproducibility through updated notebook counts and formatting. Added a wage-gap analysis notebook exploring heterogenous effects by sex with Double Lasso, featuring data loading, feature engineering, and a Double Lasso regression implementation to study socio-economic interactions. These updates improve teaching relevance, reproducibility, and the ability to derive robust insights from real-world data.
September 2025
August 2025
8 Commits • 4 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on building course infrastructure, materials organization, and data assets for the CausalAI-Course project. The month prioritized deliverables that improve onboarding, reproducibility, and practical experimentation for learners and researchers. No major bugs fixed in this period based on the provided data; emphasis was on feature delivery and documentation improvements that support long-term maintainability and scalability.
August 2025
8 Commits • 4 Features
Aug 1, 2025Monthly summary for 2025-08 focusing on building course infrastructure, materials organization, and data assets for the CausalAI-Course project. The month prioritized deliverables that improve onboarding, reproducibility, and practical experimentation for learners and researchers. No major bugs fixed in this period based on the provided data; emphasis was on feature delivery and documentation improvements that support long-term maintainability and scalability.
Activity
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Quality Metrics
Correctness91.8%
Maintainability87.2%
Architecture89.6%
Performance84.8%
AI Usage34.2%
Skills & Technologies
Programming Languages
CSSCSVHTMLJuliaJupyter NotebookMarkdownPythonRShell
Technical Skills
BootstrapCausal InferenceCross-validationData AnalysisData EngineeringData IngestionData ProcessingData ScienceDirectory OrganizationDocumentationEconometricsFile System ManagementFrontend DevelopmentJuliaJupyter
Repositories Contributed To
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