📅 2023-03-09 — Session: Executed and Analyzed Regression and Causal Inference Models
🕒 21:00–22:50
🏷️ Labels: Regression Analysis, Causal Inference, Data Visualization, Mock Datasets, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to execute and analyze regression and causal inference models using mock datasets to evaluate treatment effects, biases, and confounding variables.
Key Activities
- Developed a Python script for covariate analysis and visualization, focusing on treatment types across regions.
- Generated a mock dataset for regression analysis, including treatment and control groups.
- Created an outline for a memo on violence information outcomes, detailing data quality and matching procedures.
- Conducted quality assessment of a violence information dataset, highlighting data sources and limitations.
- Provided an overview of matching procedures and diagnostics using Python notebooks.
- Structured the experimentation process for data analysis, including function definitions and result plotting.
- Conducted numerical tests with mock data for causal inference, evaluating matching and regression methods.
- Concluded the regression models and causal inference study, summarizing findings and implications.
Achievements
- Successfully executed scripts and notebooks for regression and causal inference analysis.
- Generated insights into the robustness of regression models and treatment effects.
- Prepared structured documentation and memos for violence data analysis.
Pending Tasks
- Further investigation into the implications of biases and confounding variables on regression models.
- Continue refining matching procedures and diagnostics for more accurate analysis outcomes.