classic_rct_gamma_26
A pre-configured classic RCT dataset with a gamma outcome.
n=10000, split=0.5, mean uplift ~10%.
Includes deterministic user_id and ancillary columns.
Parameters
- seed (
int) – Random seed. - add_pre (
bool) – Whether to generate a pre-period covariate ('y_pre'). - beta_y (
array - like) – Linear coefficients for confounders in the outcome model. - outcome_depends_on_x (
bool) – Whether to add default effects for confounders if beta_y is None. - include_oracle (
bool) – Whether to include oracle ground-truth columns like 'cate', 'propensity', etc. - return_causal_data (
bool) – Whether to return a CausalData object. - n (
int) – Number of samples. - split (
float) – Proportion of samples assigned to the treatment group. - outcome_params (
dict) – Gamma outcome parameters, e.g. {"shape": 2.0, "scale": {"A": 15.0, "B": 16.5}}. - add_ancillary (
bool) – Whether to add standard ancillary columns (age, platform, etc.). - deterministic_ids (
bool) – Whether to generate deterministic user IDs. - **kwargs – Additional arguments passed to
classic_rct_gamma.
Returns
CausalData or DataFrame–