causalis.scenarios.classic_rct.dgp.generate_classic_rct_26generate_classic_rct_26
A pre-configured classic RCT dataset with 3 binary confounders.
n=10000, split=0.5, outcome is conversion (binary). Baseline control p=0.10
and treatment p=0.11 are set on the log-odds scale (X=0), so marginal rates
and ATE can differ once covariate effects are included. Includes a
deterministic user_id column.
Parameters
- seedint, default=42
Random seed.
- add_prebool, default=False
Whether to generate a pre-period covariate (‘y_pre’) and include prognostic signal from X.
- beta_yarray-like, optional
Linear coefficients for confounders in the outcome model.
- outcome_depends_on_xbool, default=True
Whether to add default effects for confounders if beta_y is None.
- 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.
- nint, default=10000
Number of samples.
- splitfloat, default=0.5
Proportion of samples assigned to the treatment group.
- outcome_paramsdict, optional
Binary outcome parameters, e.g. {“p”: {“A”: 0.10, “B”: 0.11}}.
- add_ancillarybool, default=False
Whether to add standard ancillary columns (age, platform, etc.).
- deterministic_idsbool, default=True
Whether to generate deterministic user IDs. **kwargs : Additional arguments passed to
generate_classic_rct.
Returns
CausalData or pd.DataFrame
Canonical target
causalis.scenarios.classic_rct.dgp.generate_classic_rct_26
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