causalis.scenarios.multi_unconfoundedness.dgp

Module Contents

Functions

generate_multitreatment_gamma_26

Pre-configured multi-treatment dataset with Gamma-distributed outcome.

generate_multitreatment_binary_26

Pre-configured multi-treatment dataset with Binary outcome.

generate_multitreatment_irm_26

API

causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_gamma_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) → Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]

Pre-configured multi-treatment dataset with Gamma-distributed outcome.

  • 3 treatment classes: control + 2 treatments

  • 8 confounders with realistic marginals

  • Gamma outcome with log-link linear confounding

  • Heterogeneous treatment effects and correlated confounders via Gaussian copula

causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_binary_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) → Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]

Pre-configured multi-treatment dataset with Binary outcome.

  • 3 treatment classes: control + 2 treatments

  • 8 confounders with realistic marginals

  • Binary outcome with logistic-link linear confounding

  • Heterogeneous treatment effects and correlated confounders via Gaussian copula

causalis.scenarios.multi_unconfoundedness.dgp.generate_multitreatment_irm_26(n: int = 100000, seed: int = 42, include_oracle: bool = False, return_causal_data: bool = True) → Union[pandas.DataFrame, causalis.data_contracts.multicausaldata.MultiCausalData]