📅 2023-02-28 — Session: Explored Statistical Methods for Treatment Effects
🕒 05:40–06:25
🏷️ Labels: Linear Regression, Treatment Effect, Python, Statistics, Data Analysis
📂 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 matching procedures. The session also covered relevant software tools for data analysis.
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), incorporating covariates and regression coefficients.
- Outlined steps to calculate the standardized mean difference (SMD) using Python’s StandardScaler and NumPy.
- Provided mathematical expressions for variance and SMD, including Python code snippets.
- Explored methods for calculating the difference in means between treatment and control groups using Pandas, including alternatives to using
.loc[1].
Achievements:
- Clarified the mathematical foundation for treatment effect analysis.
- Developed Python workflows for calculating SMD and difference in means.
Pending Tasks:
- Further exploration of software tools for advanced statistical modeling.
- Implementation of discussed methods in a real-world dataset to validate findings.