MixedLogit.apply_intervention

MixedLogit.apply_intervention(new_choice_df, new_utility_equations=None, fit_kwargs=None)

Apply intervention by changing observable attributes or market structure.

This method supports two intervention strategies:

1. Observable attribute changes: Uses the existing fitted model and modifies observable inputs (e.g., prices, features) to simulate how choices change. Uses posterior predictive sampling with the existing random coefficient draws.

2. Product removal/addition: Allows alternatives to be added or removed from the choice set. This re-specifies and re-estimates the model.

Parameters:

new_choice_dfpd.DataFrame

The new dataset reflecting changes to observable attributes or product availability.

new_utility_equationslist[str] | None, optional

An updated list of utility specifications for each alternative, if different from the original model. If None, the original equations are reused and only the data is changed.

fit_kwargsdict | None, optional

Keyword arguments for sampling if refitting the model.

Returns:

az.InferenceData

The posterior or full predictive distribution under the intervention.