nikbearbrown/INFO_7390_Art_and_Science_of_Data
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationDoWhyFeature Engineering
SahitiN
PROFILE
Sahitin
Developed the Causal Inference Notebook Suite for the nikbearbrown/INFO_7390_Art_and_Science_of_Data repository, delivering three end-to-end Jupyter notebooks focused on education-to-income, study time-to-performance, and COVID-19 mortality analyses. The work integrated data preprocessing, exploratory data analysis, and DAG construction, applying causal modeling techniques such as DoWhy, regression, propensity score matching, and instrumental variables. Emphasized reproducibility and transparency by including robustness checks and practical examples throughout. Leveraged Python, Pandas, and Scikit-learn to create reusable, teaching-oriented workflows that support onboarding and enable researchers to conduct policy-relevant causal inference studies with clear, well-documented analytics pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
7,268
Activity Months1
Your Network
159 peopleShared Repositories
18
Work History
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 — Delivered the Causal Inference Notebook Suite in nikbearbrown/INFO_7390_Art_and_Science_of_Data, featuring three end-to-end notebooks for education-to-income, study time-to-performance, and COVID-19 mortality. The work covers data preprocessing, exploratory data analysis, DAG construction, and causal modeling with DoWhy, regression, propensity score matching, and instrumental variables, including robustness checks and practical examples. No major bugs fixed this month; main focus was on delivering production-grade, teaching-friendly notebooks and reproducible workflows that enable researchers to perform transparent causal analyses and accelerate policy-relevant research. Key commits: 023188a5135341d905c485e589f6f0bbbf461863, 20be833d9b3e27ace207295beb7ab90c2b14aa84, 51a6dc832270b89afca109f1257e0cab7f6c7a16
3 Commits • 1 Features
Apr 1, 2025April 2025 — Delivered the Causal Inference Notebook Suite in nikbearbrown/INFO_7390_Art_and_Science_of_Data, featuring three end-to-end notebooks for education-to-income, study time-to-performance, and COVID-19 mortality. The work covers data preprocessing, exploratory data analysis, DAG construction, and causal modeling with DoWhy, regression, propensity score matching, and instrumental variables, including robustness checks and practical examples. No major bugs fixed this month; main focus was on delivering production-grade, teaching-friendly notebooks and reproducible workflows that enable researchers to perform transparent causal analyses and accelerate policy-relevant research. Key commits: 023188a5135341d905c485e589f6f0bbbf461863, 20be833d9b3e27ace207295beb7ab90c2b14aa84, 51a6dc832270b89afca109f1257e0cab7f6c7a16
April 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Causal InferenceData AnalysisData PreprocessingData VisualizationDoWhyFeature EngineeringMachine LearningMatplotlibNetworkXNumPyPandasPythonScikit-learnSeabornStatistical Modeling
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