Lancelot39/Causal-Copilot
Jan 2025 – Mar 2025
3 Months active
Languages Used
PythonSQLText
Technical Skills
Backend DevelopmentCausal InferenceData AnalysisMachine LearningPythonDependency Management
aseemdandgaval
PROFILE
Aseemdandgaval
Aseem Dandgaval contributed to Lancelot39/Causal-Copilot by developing and refining causal inference capabilities over three months. He integrated Double Robust Learning estimators and implemented an Instrumental Variables framework, enhancing the system’s ability to deliver robust causal analysis. Using Python and Scikit-learn, Aseem improved API workflows, stabilized estimation pipelines, and addressed parameter handling for both DRL and IV methods. He also enhanced data visualization by increasing figure sizes for key plots, supporting clearer interpretation of heterogeneous and conditional treatment effects. His work demonstrated depth in backend development, machine learning, and data analysis, resulting in a more reliable and business-ready analytics platform.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
13Total
Bugs
1
Commits
13
Features
4
Lines of code
788
Activity Months3
Your Network
16 peopleShared Repositories
16
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Lancelot39/Causal-Copilot. Focused on improving visualization readability in the causal analysis module to support clearer interpretation of model results. Key work centered on increasing the figure sizes for two plotting functions (plot_hte_dist and plot_cate_violin) to enhance detail in generated graphs used for HTE and CATE assessment. No major bugs fixed this month; effort was aligned with improving report quality and analyst productivity. Change is captured in a single commit (a5f4d9d46ff5bb7c24fb8cbb8433693f875bcf3a).
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Lancelot39/Causal-Copilot. Focused on improving visualization readability in the causal analysis module to support clearer interpretation of model results. Key work centered on increasing the figure sizes for two plotting functions (plot_hte_dist and plot_cate_violin) to enhance detail in generated graphs used for HTE and CATE assessment. No major bugs fixed this month; effort was aligned with improving report quality and analyst productivity. Change is captured in a single commit (a5f4d9d46ff5bb7c24fb8cbb8433693f875bcf3a).
March 2025
February 2025
9 Commits • 2 Features
Feb 1, 2025Concise February 2025 monthly summary for Lancelot39/Causal-Copilot focusing on delivering business value through robust IV-based causal inference and updated demo readiness. The month centers on implementing an Instrumental Variables (IV) causal inference framework with DRIV/DRL integration, stabilizing estimation pipelines, and preparing the project for production-grade usage with refreshed dependencies.
February 2025
9 Commits • 2 Features
Feb 1, 2025Concise February 2025 monthly summary for Lancelot39/Causal-Copilot focusing on delivering business value through robust IV-based causal inference and updated demo readiness. The month centers on implementing an Instrumental Variables (IV) causal inference framework with DRIV/DRL integration, stabilizing estimation pipelines, and preparing the project for production-grade usage with refreshed dependencies.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01): Delivered DRL-based causal inference integration in Lancelot39/Causal-Copilot, enabling end-to-end DRL estimation and API enhancements while preserving ATE/ATT calculations. Fixed DRL parameter selector bug to improve discrete node handling. These efforts deliver more robust causal estimates, streamlined API usage, and a foundation for scalable, data-driven decision support.
3 Commits • 1 Features
Jan 1, 2025January 2025 (Month: 2025-01): Delivered DRL-based causal inference integration in Lancelot39/Causal-Copilot, enabling end-to-end DRL estimation and API enhancements while preserving ATE/ATT calculations. Fixed DRL parameter selector bug to improve discrete node handling. These efforts deliver more robust causal estimates, streamlined API usage, and a foundation for scalable, data-driven decision support.
January 2025
Activity
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Quality Metrics
Correctness84.6%
Maintainability84.6%
Architecture81.6%
Performance68.6%
AI Usage35.4%
Skills & Technologies
Programming Languages
PythonSQLText
Technical Skills
Backend DevelopmentCausal InferenceData AnalysisData VisualizationDependency ManagementEconometricsHyperparameter TuningMachine LearningMatplotlibPythonRefactoringScikit-learnSeabornSoftware Engineering
Repositories Contributed To
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