📅 2023-03-09 — Session: Implemented Mean Differences and Matching Evaluation
🕒 20:10–20:40
🏷️ Labels: Python, Data Analysis, Mean Differences, Treatment Effects, Empirical Studies
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The goal of this session was to implement and refine Python code for calculating mean differences in treatment analysis and to evaluate the success of matching procedures in empirical studies.
Key Activities:
- Developed a Python function to compute mean differences between treated and control units, focusing on efficiency by eliminating nested loops and highlighting matched units.
- Modified existing scripts to add a column indicating inclusion of all or matched units in mean difference calculations.
- Created a loop to display grouped tables of mean and standard deviation values by levels from the ‘mean_diffs’ DataFrame.
- Evaluated the effectiveness of matching procedures in reducing covariate differences, using statistical analysis to assess the success of these methods.
- Discussed the importance of covariate balance in empirical studies, including methods for comparing covariate means and implications for treatment effect attribution.
- Corrected syntax for saving figures in Python using Matplotlib, with a focus on dynamic file naming using f-strings.
Achievements:
- Successfully implemented and refined code for calculating mean differences and assessing matching procedures.
- Enhanced understanding of covariate balance and its implications for empirical research.
Pending Tasks:
- Conduct further sensitivity analyses to ensure robustness of the matching procedures and data quality checks.