📅 2023-02-28 — Session: Analyzed Treatment Effects and Statistical Methods
🕒 05:40–06:30
🏷️ Labels: Linear Regression, Treatment Effect, Python, Statistical Modeling
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore statistical methods for analyzing treatment effects, focusing on linear regression, treatment effects, confounding factors, and relevant software tools.
Key Activities
- Discussed linear regression and treatment effects, including confounding factors and matching procedures.
- Detailed the mathematical expression for calculating the Adjusted Treatment Effect (ATE) using covariates.
- Outlined steps to calculate the Standardized Mean Difference (SMD) using Python’s StandardScaler and NumPy.
- Provided mathematical formulas for variance and SMD, including Python code snippets.
- Explained methods for calculating the difference in means between treatment and control groups using Pandas.
Achievements
- Clarified the use of linear regression and treatment effects in statistical modeling.
- Developed a clear understanding of calculating ATE and SMD with Python.
Pending Tasks
- Further exploration of alternative methods for calculating mean differences in Pandas.
- Review and application of discussed software tools for data analysis.