Implemented ATE Calculation in Python DataFrame

• Day: 2023-02-22
• Time: 01:45 to 02:55
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: ATE, Linear Regression, Dataframe, Python, Statistics

Description

Session Goal

The session aimed to implement and validate the calculation of the Average Treatment Effect (ATE) within a Python DataFrame, leveraging statistical models and programming techniques.

Key Activities

• Statistical Modeling: Explored the formal demonstration of linkages in linear regression and ATE calculation, focusing on the relationship between treatment propensity and outcome variables.
• Mathematical Derivation: Derived the ATE as a function of parameter a_1, simplifying expressions to reach a linear formulation.
• Programming Implementation: Modified an experiment function to include ATE values in a DataFrame using Pandas, and created a new DataFrame for ATE calculation, concatenating it with existing data.

Achievements

• Successfully integrated ATE calculations into the experiment code, enabling the analysis of treatment effects within the dataset.
• Enhanced understanding of statistical modeling and its application in data analysis.

Pending Tasks

• Validate the accuracy of the ATE calculations with additional test cases.
• Optimize the DataFrame manipulation for performance improvements.

Evidence

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• event_ids: []