Causalis

Generate a classic RCT dataset with three binary confounders: platform_ios, country_usa, and source_paid.

n (int) – Number of samples to generate.
split (float) – Proportion of samples assigned to the treatment group.
random_state (int) – Random seed for reproducibility.
outcome_params (dict) – Parameters defining baseline rates/means and treatment effects. e.g., {"p": {"A": 0.1, "B": 0.15}} for binary.
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.
prognostic_scale (float) – Scale of nonlinear prognostic signal (passed to generate_rct).
pre_corr (float) – Target correlation for y_pre (passed to generate_rct).
return_causal_data (bool) – Whether to return a CausalData object instead of a pandas.DataFrame.
add_ancillary (bool) – Whether to add standard ancillary columns (age, platform, etc.).
deterministic_ids (bool) – Whether to generate deterministic user IDs.
include_oracle (bool) – Whether to include oracle ground-truth columns like 'cate', 'propensity', etc.
**kwargs – Additional arguments passed to generate_rct.