CATE vs Propensity about causallift HOT 5 CLOSED

Propensity score is used to compute CATE (uplift score).
The range of propensity score, which is estimated probability to be treated, is between 0 and +1.
CATE is difference of probability values, so the range is between -1 and +1.

You can find explanation at:
https://github.com/Minyus/causallift/blob/develop/README.md

from causallift.

Is it necessary to calculate propensity?
I have an A/B test, therefore, I know which samples are treated and which are not.
Later I plan to use Causallift model for later predictions on new data. If I do not need propensity for now since I use A/B test, do I need it for prediction?
May I ask you to explain what is this propensity for and what does it mean?

Thanks

from causallift.

For A/B test (RCT) data, propensity score estimation is not needed, so you can set enable_ipw False.

CausalLift(train_df, test_df, enable_ipw=False)

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.

CausalLift(train_df, test_df, enable_ipw=True)

from causallift.

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
That is true?

from causallift.

That is true?

No.

I added explanation in the following sections in README.md.

https://github.com/Minyus/causallift#how-causallift-works
https://github.com/Minyus/causallift#how-to-run-inferrence-prediction-of-cate-for-new-data-with-treatment-and-outcome-unknown

enable_ipw flag is used only during training.

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