Comments (3)
Minyus
commented on August 21, 2024
Yes, the generated data is random data based on parameters.
For more details, please see the comments in the code:
https://github.com/Minyus/causallift/blob/develop/src/causallift/generate_data.py
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Jami1141
commented on August 21, 2024
Thanks for your reply. Do we need to generate propensity feature for this generated data?
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Minyus
commented on August 21, 2024
It depends.
If you want to use simulated data for observational data, propensity_coef can be used.
If you want to use simulated data for A/B Test (Randomized Controlled Trial), propensity_coef is not needed.
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Related Issues (20)
- Using Causallift for later predition HOT 23
- Order of features in train and new data HOT 5
- Some clarification about the code HOT 14
- Is there a causal modeling for multiple treatments? HOT 5
- cl.estimate_cate_by_2_models() does not work with XGBoost version 1.0.2 HOT 4
- Question Code & Result HOT 6
- Examples with Observational data HOT 2
- Error regarding to base_score HOT 2
- How do we can give separtae scale_pos_weight for two separated models? HOT 1
- A question about train.df and test.df HOT 1
- ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). HOT 2
- TypeError: run() missing 1 required positional argument: 'hook_manager' HOT 2
- XGBooster Invalid missing value: null HOT 1
- pipeline issue (Kedro?) HOT 1
- Getting Json Formatter error HOT 1
- CATE vs Propensity HOT 5
- The robustness of uplift HOT 1
- The effect of Stratify = ['Treatment'] HOT 3
- A clear explanation HOT 1
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