📅 2023-03-09 — Session: Implemented and Evaluated Matching Techniques in Python
🕒 20:10–20:40
🏷️ Labels: Python, Data Analysis, Matching Techniques, Empirical Studies, Mean Differences
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to implement and evaluate matching techniques in Python to analyze treatment effects in empirical studies.
Key Activities
- Developed a Python function to compute mean differences between treated and control units, focusing on matched units to enhance data analysis efficiency.
- Modified existing scripts to add a column indicating whether calculations include all units or only matched units.
- Created loops to display grouped tables by levels, showing mean and standard deviation values.
- Evaluated the success of matching procedures in reducing covariate differences, using statistical methods to assess balance in treated and control groups.
- Corrected syntax for saving figures in Python using Matplotlib, employing f-strings for dynamic file naming.
Achievements
- Successfully implemented a streamlined process for calculating mean differences, improving the efficiency of data analysis.
- Enhanced the presentation of statistical data by grouping and displaying relevant metrics.
- Assessed and confirmed the effectiveness of matching procedures in empirical studies.
- Improved data visualization techniques through corrected syntax.
Pending Tasks
- Conduct further sensitivity analyses to ensure robustness of results.
- Perform additional data quality checks to validate findings.