Enhanced ATE visualization with dynamic parameters

📅 2023-02-22 — Session: Enhanced ATE visualization with dynamic parameters

🕒 03:05–03:50
🏷️ Labels: Python, Data Visualization, ATE, Function Modification, Seaborn, Matplotlib
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The aim of this session was to enhance the visualization of the Average Treatment Effect (ATE) in Python by developing and refining functions that plot ATE lines on various types of plots, including box plots and scatter plots.

Key Activities

• Developed a Python function to define and plot ATE using seaborn, focusing on box plots to visualize treatment effects.
• Created a scatterplot function that incorporates ATE lines, using regression coefficients and error bars to depict standard deviations.
• Implemented the add_ATE_line function to add ATE lines to plots, with examples of integration into scatter plots.
• Modified the add_ATE_line function to accept arrays for plotting multiple ATE lines and to dynamically sweep parameters, enhancing flexibility in visualization.
• Updated the add_ATE_line function to include default parameters for more streamlined plotting of ATE values.

Achievements

• Successfully developed and modified functions to visualize ATE with enhanced flexibility and dynamic parameter handling.
• Improved the plotting capabilities to allow for multiple and dynamic ATE lines, facilitating better analysis of treatment effects.

Pending Tasks

• Further testing and validation of the enhanced functions with real-world datasets to ensure robustness and accuracy.