SyntheticControl.effect_summary#

SyntheticControl.effect_summary(*, window='post', direction='increase', alpha=0.05, cumulative=True, relative=True, min_effect=None, treated_unit=None, period=None, prefix='Post-period', **kwargs)[source]#

Generate a decision-ready summary of causal effects for Synthetic Control.

Parameters:

window (str, tuple, or slice, default="post") – Time window for analysis: - “post”: All post-treatment time points (default) - (start, end): Tuple of start and end times (handles both datetime and integer indices) - slice: Python slice object for integer indices
direction ({"increase", "decrease", "two-sided"}, default="increase") – Direction for tail probability calculation (PyMC only, ignored for OLS).
alpha (float, default=0.05) – Significance level for HDI/CI intervals (1-alpha confidence level).
cumulative (bool, default=True) – Whether to include cumulative effect statistics.
relative (bool, default=True) – Whether to include relative effect statistics (% change vs counterfactual).
min_effect (float, optional) – Region of Practical Equivalence (ROPE) threshold (PyMC only, ignored for OLS).
treated_unit (str, optional) – For multi-unit experiments, specify which treated unit to analyze. If None and multiple units exist, uses first unit.
period ({"intervention", "post", "comparison"}, optional) – Ignored for Synthetic Control (two-period design only).
prefix (str, optional) – Prefix for prose generation. Defaults to “Post-period”.
kwargs (Any)

Returns:

Object with .table (DataFrame) and .text (str) attributes

Return type:

EffectSummary