causalis.scenarios.cuped.dgp.generate_cuped_tweedie_26generate_cuped_tweedie_26
Gold standard Tweedie-like DGP with mixed marginals and structured HTE. Features many zeros and a heavy right tail. Includes two pre-period covariates by default: ‘y_pre’ and ‘y_pre_2’. Wrapper for make_tweedie().
Parameters
- nint, default=10000
Number of samples to generate.
- seedint, default=42
Random seed.
- add_prebool, default=True
Whether to add pre-period covariates.
- pre_namestr, default=”y_pre”
Name of the first pre-period covariate column.
- pre_name_2str, optional
Name of the second pre-period covariate column. Defaults to
f"{pre_name}_2".- pre_target_corrfloat, default=0.82
Target correlation between the first pre covariate and post-outcome y in control group.
- pre_target_corr_2float, optional
Target correlation for the second pre covariate. Defaults to a moderate value based on
pre_target_corrto reduce collinearity.- pre_specPreCorrSpec, optional
Detailed specification for pre-period calibration (transform, method, etc.).
- include_oraclebool, default=False
Whether to include oracle ground-truth columns like ‘cate’, ‘propensity’, etc.
- return_causal_databool, default=True
Whether to return a CausalData object.
- theta_logfloat, default=0.38
The log-uplift theta parameter for the treatment effect.
Returns
pd.DataFrame or CausalData
Canonical target
causalis.scenarios.cuped.dgp.generate_cuped_tweedie_26
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