📅 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.