📅 2023-02-22 — Session: Implemented ATE in DataFrame and Explored Linear Regression
🕒 01:45–03:00
🏷️ Labels: Linear Regression, ATE, Dataframe, Python, Statistics
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to explore the linkages in linear regression models and implement the Average Treatment Effect (ATE) in a Python DataFrame for experimental data analysis.
Key Activities:
- Provided a formal demonstration of the mathematical linkages in linear regression and the calculation of ATE, focusing on treatment propensity and outcome variables.
- Detailed the derivation of ATE as a function of parameter a_1, simplifying expressions to reach a linear form.
- Implemented code to add ATE values to an experiment’s DataFrame using Pandas, modifying the DataFrame structure to incorporate parameters like
a1andp1. - Created a new DataFrame to hold ATE calculations and demonstrated concatenation with existing data using pandas.
Achievements:
- Successfully integrated ATE calculations into the experimental data workflow, enhancing the analysis capabilities within the Python environment.
Pending Tasks:
- Further validation of the ATE implementation and exploration of its implications on experimental results.
- Documentation of the implemented changes for future reference and reproducibility.