Developed Python Code for Propensity Score Matching

• Day: 2023-02-15
• Time: 14:10 to 16:15
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Propensity Score Matching, Infrastructure Investment, Data Analysis

Description

Session Goal: The session aimed to develop and refine Python code for implementing Propensity Score Matching (PSM) in infrastructure investment studies.

Key Activities:

• Reviewed literature on the relationship between infrastructure investment and economic growth, focusing on the use of propensity score matching (PSM) in studies.
• Detailed the methodology for evaluating the impact of infrastructure investment, including sample selection and outcome variables.
• Developed Python code snippets for estimating treatment effects using regression analysis and PSM.
• Corrected and refined code for logistic regression and nearest neighbor matching.
• Addressed programming errors and clarified the use of PSM in Python, utilizing packages like causalnexus and sklearn.
• Discussed the importance of incorporating confounders in propensity score models to enhance accuracy.

Achievements:

• Successfully developed and corrected Python code for PSM, including logistic regression and nearest neighbor matching.
• Enhanced understanding of PSM implementation in Python and its application in infrastructure investment studies.

Pending Tasks:

• Further testing and validation of the developed Python code in real-world datasets.
• Exploration of additional Python packages for PSM to improve efficiency and accuracy.

Evidence

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• event_ids: []