nikbearbrown/INFO_7390_Art_and_Science_of_Data
Feb 2026 – Feb 2026
1 Month active
Languages Used
Python
Technical Skills
Jupyter notebookscausal inferencedata analysisstatistical modeling
Nishanth102002
PROFILE
Nishanth102002
Nishanth Royee Balachandrababu developed a Causal Inference IPW Notebook for the nikbearbrown/INFO_7390_Art_and_Science_of_Data repository, focusing on estimating the causal effect of exercise interventions on weight loss using observational health data. Leveraging Python, Jupyter notebooks, and statistical modeling, Nishanth implemented inverse probability weighting to address selection bias, enabling more accurate causal analysis. The notebook includes runnable examples and thorough documentation, supporting reproducibility and easing onboarding for new analysts. This work provided a reusable template for causal inference, allowing researchers to derive actionable insights from complex datasets and informing the design and evaluation of health intervention programs with data-driven evidence.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,149
Activity Months1
Your Network
155 peopleShared Repositories
18
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for the INFO_7390 Art and Science of Data project. Key feature delivered: a Causal Inference IPW Notebook for Exercise and Weight Loss demonstrating how to apply inverse probability weighting to estimate causal effects from observational data, addressing selection bias in health studies. This work enhances data-driven decision making for health interventions and provides a reusable notebook template for causal analysis in the dataset.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for the INFO_7390 Art and Science of Data project. Key feature delivered: a Causal Inference IPW Notebook for Exercise and Weight Loss demonstrating how to apply inverse probability weighting to estimate causal effects from observational data, addressing selection bias in health studies. This work enhances data-driven decision making for health interventions and provides a reusable notebook template for causal analysis in the dataset.
February 2026
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Jupyter notebookscausal inferencedata analysisstatistical modeling
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