d2cml-ai/CausalAI-Course
Nov 2024 – Dec 2024
2 Months active
Languages Used
JuliaJupyter NotebookMarkdownPythonRJSONSVG
Technical Skills
BootstrappingCausal InferenceCode OrganizationDAGsData AnalysisData Preparation
nicosil02
PROFILE
Nicosil02
Contributed to the d2cml-ai/CausalAI-Course repository by developing an end-to-end causal inference analysis pipeline and enhancing reproducibility for teaching and research workflows. Built comprehensive Jupyter notebooks in Python and R to support data preprocessing, feature engineering, and model evaluation, including OLS regression, regression trees, and Double Lasso methods. Implemented heterogeneous treatment effects analysis using causal trees and forests, with bias and ATE evaluation, and improved visualization outputs in HTML and SVG formats. Streamlined environment setup and project organization, resolving workflow issues and stabilizing version control, which enabled faster onboarding, more reliable analyses, and clearer interpretation of policy-relevant causal effects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
4
Lines of code
50,290
Activity Months2
Your Network
91 peopleWork History
December 2024
4 Commits • 2 Features
Dec 1, 2024December 2024 Monthly Summary for d2cml-ai/CausalAI-Course. Key features delivered include heterogeneous treatment effects analysis across models with bias/ATE evaluation (via causal trees/forests) and visualization of predicted versus actual effects, as well as project setup and visualization enhancements (environment/workspace setup, notebook updates, visualization HTML/SVG output refinements). Major bugs fixed involved stabilizing the development workflow by resolving git clone errors and correcting notebook execution counts. Overall impact: enhanced reliability, reproducibility, and interpretability of causal analysis workflows, enabling faster decision-making and onboarding for new contributors. Technologies and skills demonstrated: causal inference techniques (heterogeneous effects, ATE evaluation), model comparison, data bias analysis, advanced visualizations, environment/tooling automation, Jupyter notebooks, and version control.
4 Commits • 2 Features
Dec 1, 2024December 2024 Monthly Summary for d2cml-ai/CausalAI-Course. Key features delivered include heterogeneous treatment effects analysis across models with bias/ATE evaluation (via causal trees/forests) and visualization of predicted versus actual effects, as well as project setup and visualization enhancements (environment/workspace setup, notebook updates, visualization HTML/SVG output refinements). Major bugs fixed involved stabilizing the development workflow by resolving git clone errors and correcting notebook execution counts. Overall impact: enhanced reliability, reproducibility, and interpretability of causal analysis workflows, enabling faster decision-making and onboarding for new contributors. Technologies and skills demonstrated: causal inference techniques (heterogeneous effects, ATE evaluation), model comparison, data bias analysis, advanced visualizations, environment/tooling automation, Jupyter notebooks, and version control.
December 2024
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for the d2cml-ai/CausalAI-Course project. Delivered an end-to-end causal inference analysis pipeline and streamlined notebook workflows to support reproducible teaching materials and policy-relevant insights. Key outputs include Double Lasso and DAG-based analysis to quantify wage effects linked to college graduation and health-related outcomes, plus comprehensive notebooks for data preprocessing, feature engineering, and model evaluation. This work enhances interpretability, reproducibility, and onboarding for students and researchers.
November 2024
8 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for the d2cml-ai/CausalAI-Course project. Delivered an end-to-end causal inference analysis pipeline and streamlined notebook workflows to support reproducible teaching materials and policy-relevant insights. Key outputs include Double Lasso and DAG-based analysis to quantify wage effects linked to college graduation and health-related outcomes, plus comprehensive notebooks for data preprocessing, feature engineering, and model evaluation. This work enhances interpretability, reproducibility, and onboarding for students and researchers.
Activity
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Quality Metrics
Correctness77.4%
Maintainability76.6%
Architecture73.4%
Performance73.4%
AI Usage25.0%
Skills & Technologies
Programming Languages
JSONJuliaJupyter NotebookMarkdownPythonRSVG
Technical Skills
BootstrappingCausal InferenceCode OrganizationDAGsData AnalysisData PreparationData PreprocessingData VisualizationDecision TreesDouble LassoEconometricsEnvironment SetupFeature EngineeringFile ManagementJulia
Repositories Contributed To
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