GradedInterventionTimeSeries.__init__#

GradedInterventionTimeSeries.__init__(data, y_column, treatment_names, base_formula, model=None, **kwargs)[source]#

Initialize experiment with data and unfitted model (standard CausalPy pattern).

This method: 1. Validates inputs and builds baseline design matrix 2. Estimates transform parameters for each treatment 3. Applies transforms and builds full design matrix 4. Calls model.fit(X_full, y) 5. Extracts results for visualization and analysis

Parameters:

  • data (DataFrame) – Time series data.

  • y_column (str) – Name of outcome variable.

  • treatment_names (list[str]) – List of treatment variable names (e.g., [“comm_intensity”]).

  • base_formula (str) – Patsy formula for baseline model.

  • model (TransferFunctionOLS) – UNFITTED model with configuration for transform estimation.