causalis.scenarios.iv.dgp API Reference | Causalis

function

causalis.scenarios.iv.dgp.generate_offer_iv_26

generate_offer_iv_26

Generate a realistic IV dataset with a positive business effect.

This scenario mimics an offer-eligibility experiment in a customer product:

offer_eligible is the binary instrument (Z). It affects whether customers can accept an offer, but it has no direct outcome effect in the DGP.
accepted_offer is the binary endogenous treatment (D).
net_revenue_90d is a continuous outcome (Y) with a positive heterogeneous treatment effect among customers induced into treatment by eligibility.

The DGP follows a structural model where unobserved confounders $U$ affect both treatment $D$ and outcome $Y$ , but not the instrument $Z$ :

Z &= f_Z(X, \epsilon_Z) \ D &= f_D(Z, X, U, \epsilon_D) \ Y &= f_Y(D, X, U, \epsilon_Y)

Parameters

nint, default 20_000: Number of observations to generate.
seedint, default 42: Random seed for reproducibility.
include_oraclebool, default True: Whether to include latent variables (ITE, LATE, etc.) in the output DataFrame.
return_causal_databool, default True: If True, returns an :class:~causalis.data_contracts.iv_causal_data.IVCausalData object. If False, returns a :class:pandas.DataFrame.
deterministic_idsbool, default True: Whether to generate stable, deterministic user IDs.

Returns

Union[pd.DataFrame, IVCausalData]

The generated dataset.

Examples

Generate data as a pandas DataFrame

Canonical target

causalis.scenarios.iv.dgp.generate_offer_iv_26

Sections

ParametersReturnsExamples

data

causalis.scenarios.iv.dgp.__all__

Value: ['generate_offer_iv_26']

[‘generate_offer_iv_26’]

Canonical target

causalis.scenarios.iv.dgp.__all__