SyntheticDifferenceInDifferences.effect_summary#

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

Generate a decision-ready summary of causal effects for SDiD.

Parameters:

  • group (Literal['prior', 'posterior']) – Which draw group to summarize. "prior" requires sample_prior_predictive() and produces prior-appropriate prose — under a neutral prior, P(effect > 0) should sit near 0.5, so a tail probability far from 0.5 flags a design-matrix or prior-specification problem rather than a causal finding. "posterior" requires fit().

  • window (Union[Literal['post'], tuple, slice]) – Time window for analysis.

  • direction (Literal['increase', 'decrease', 'two-sided']) – Direction for tail probability calculation.

  • alpha (float) – Significance level for HDI intervals.

  • cumulative (bool) – Whether to include cumulative effect statistics.

  • relative (bool) – Whether to include relative effect statistics.

  • min_effect (float | None) – ROPE threshold.

  • treated_unit (str | None) – Which treated unit to analyze. If None, uses first unit.

  • period (Optional[Literal['intervention', 'post', 'comparison']]) – Ignored for SDiD (two-period design only).

  • prefix (str) – Prefix for prose generation. Defaults to “Post-period”.

Returns:

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

Return type:

EffectSummary