jroessler/autoum
A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects
This framework helps marketing analysts and data scientists understand the individual impact of a campaign or treatment. By inputting experimental data (like A/B test results) with binary treatment and response indicators, it evaluates various uplift modeling methods to predict which individual customers are most likely to respond positively to an intervention. This allows for more targeted marketing or customer retention efforts.
No commits in the last 6 months. Available on PyPI.
Use this if you need to compare and evaluate multiple uplift modeling algorithms to identify which customers are most likely to be positively influenced by a specific action or campaign.
Not ideal if you are looking for a general-purpose causal inference tool that doesn't specifically focus on uplift modeling with binary treatments and responses.
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Python
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Last pushed
Aug 14, 2023
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