dgp

causalis.dgp

Modules

• base –
• causaldata –
• causaldata_instrumental –
• multicausaldata –

Classes

• CausalDatasetGenerator – Generate synthetic causal inference datasets with controllable confounding,

Functions

• classic_rct_gamma – Generate a classic RCT dataset with three binary confounders and a gamma outcome.
• classic_rct_gamma_26 – A pre-configured classic RCT dataset with a gamma outcome.
• generate_classic_rct – Generate a classic RCT dataset with three binary confounders:
• generate_classic_rct_26 – A pre-configured classic RCT dataset with 3 binary confounders.
• generate_cuped_binary – Binary CUPED-oriented DGP with richer confounders and structured HTE.
• generate_cuped_tweedie_26 – Gold standard Tweedie-like DGP with mixed marginals and structured HTE.
• generate_iv_data – Generate synthetic dataset with instrumental variables.
• generate_rct – Generate an RCT dataset with randomized treatment assignment.
• make_cuped_binary_26 – Binary CUPED benchmark with richer confounders and structured HTE.
• make_cuped_tweedie – Tweedie-like DGP with mixed marginals and structured HTE.
• make_gold_linear – A standard linear benchmark with moderate confounding.
• obs_linear_26_dataset – A pre-configured observational linear dataset with 5 standard confounders.
• obs_linear_effect – Generate an observational dataset with linear effects of confounders and a constant treatment effect.