data-analysis

End-to-end data analysis for social science research is performed using Python and Stata to load data, run regressions, and export publication-ready tables and figures. It handles the entire workflow from exploratory analysis to final output generation.

Yes, DID, IV, and RDD regression analysis with panel data are fully supported. The workflow executes these specialized social science methods using either Python or Stata and outputs formatted results suitable for academic publication.

Automating regression table formatting and event study plots is handled by the workflow's output generation phase, which produces clean, publication-ready figures and tables directly from your regression results for academic papers.

The data analysis workflow supports both Python and Stata for regression modeling and specialized methods. You can leverage either environment based on your coding standards to perform the analysis and generate the required outputs.

To load a CSV dataset and run a regression with fixed effects, you specify the dataset path, dependent and independent variables, and desired fixed effects. The workflow executes the analysis and saves the resulting tables to your designated output directory.