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
During April 2025, Sandeep Nallamolu 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. He designed reproducible workflows that guide users through data preprocessing, exploratory data analysis, DAG construction, and multiple causal modeling techniques using Python, Pandas, and DoWhy. The notebooks include robustness checks and practical examples, supporting transparent and policy-relevant research. Sandeep’s work emphasized clarity and reusability, providing teaching-friendly documentation and code that enable researchers and students to efficiently perform and understand causal inference analyses.
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
133 people
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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