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CATE vs Propensity #6
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Propensity score is used to compute CATE (uplift score). You can find explanation at: |
Is it necessary to calculate propensity? Thanks |
For A/B test (RCT) data, propensity score estimation is not needed, so you can set
For observational data (data not from A/B Test or RCT), treatment should have been chosen based on a different probability (propensity score) for each sample, so IPW (Inverse Probability Weighting) using propensity score can be used optionally.
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Thanks for your respond. So it means when I use A/B test data for training the model, I do not need to have propensity but later when I want to use mode for later prediction I have to put enable_ipw= True |
No. I added explanation in the following sections in README.md. https://github.com/Minyus/causallift#how-causallift-works
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I am just following the tutorial, and I have generated two columns: CATE and Propensity. The tutorial recommends selecting users with a high uplift score which is CATE.
Is the Propensity column any use to us at all? Or can I just disregard it? The propensity may be a positive number, and CATE could be negative some times. I'm not sure how to interpret the scores when this happens.
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