Executed and Analyzed Regression and Causal Inference Models
- Day: 2023-03-09
- Time: 21:00 to 22:50
- Project: Dev
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Regression Analysis, Causal Inference, Data Visualization, Mock Datasets, Python
Description
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.
Evidence
- source_file=2023-03-09.sessions.jsonl, line_number=1, event_count=0, session_id=56431575bbfb7891b65993736fb66dd5bd162290c2be28559085b4271e29455b
- event_ids: []