📅 2023-02-22 — Session: Implemented ATE Calculation in Python DataFrame
🕒 01:45–02:55
🏷️ Labels: ATE, Linear Regression, Dataframe, Python, Statistics
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to implement and validate the calculation of the Average Treatment Effect (ATE) within a Python DataFrame, leveraging statistical models and programming techniques.
Key Activities
- Statistical Modeling: Explored the formal demonstration of linkages in linear regression and ATE calculation, focusing on the relationship between treatment propensity and outcome variables.
- Mathematical Derivation: Derived the ATE as a function of parameter a_1, simplifying expressions to reach a linear formulation.
- Programming Implementation: Modified an experiment function to include ATE values in a DataFrame using Pandas, and created a new DataFrame for ATE calculation, concatenating it with existing data.
Achievements
- Successfully integrated ATE calculations into the experiment code, enabling the analysis of treatment effects within the dataset.
- Enhanced understanding of statistical modeling and its application in data analysis.
Pending Tasks
- Validate the accuracy of the ATE calculations with additional test cases.
- Optimize the DataFrame manipulation for performance improvements.