📅 2023-02-20 — Session: Developed and Enhanced KNN Matching and Regression Functions
🕒 07:10–08:10
🏷️ Labels: KNN, Regression, Python, Data Analysis, Simulation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to address errors in matrix multiplication during regression analysis and to develop and enhance functions for KNN matching and regression analysis for treatment effect estimation.
Key Activities:
- Reflected on matrix multiplication errors in regression analysis, identifying shape incompatibility issues and suggesting troubleshooting methods.
- Developed a Python function for K-Nearest Neighbors (KNN) matching, utilizing linear assignment to create matched pairs from treated and control units.
- Created a regression analysis function to estimate the average treatment effect using matched pairs, leveraging the statsmodels library.
- Generated simulated datasets with specific characteristics, including covariates and treatment effects, using logistic and linear regression models.
- Enhanced the
knn_matching
function to process a single DataFrame, improving efficiency and usability.
Achievements:
- Successfully identified and addressed matrix multiplication errors in regression analysis.
- Developed and enhanced KNN matching functions for better data processing and treatment effect estimation.
- Generated simulated data for robust testing of analysis functions.
Pending Tasks:
- Further testing and validation of the enhanced KNN matching function with diverse datasets.
- Exploration of additional error handling strategies for regression analysis.