InterruptedTimeSeries.analyze_persistence#

InterruptedTimeSeries.analyze_persistence(hdi_prob=0.95, direction='increase')[source]#

Analyze effect persistence between intervention and post-intervention periods.

Computes mean effects, persistence ratio, and total (cumulative) impacts for both periods. The persistence ratio is the post-intervention mean effect divided by the intervention mean effect (as a decimal, e.g., 0.30 means 30% persistence, 1.5 means 150%). Note: The ratio can exceed 1.0 if the post-intervention effect is larger than the intervention effect.

Automatically prints a summary of the results.

Parameters:

hdi_prob (float, default=0.95) – Probability for HDI interval (Bayesian models only)
direction ({"increase", "decrease", "two-sided"}, default="increase") – Direction for tail probability calculation (Bayesian models only)

Returns:

Dictionary containing: - “mean_effect_during”: Mean effect during intervention period - “mean_effect_post”: Mean effect during post-intervention period - “persistence_ratio”: Post-intervention mean effect divided by intervention mean (decimal, can exceed 1.0) - “total_effect_during”: Total (cumulative) effect during intervention period - “total_effect_post”: Total (cumulative) effect during post-intervention period

Return type:

dict[str, Any]

Raises:

ValueError – If treatment_end_time is not provided (two-period design)

Examples

>>> import causalpy as cp
>>> import pandas as pd
>>> df = (
...     cp.load_data("its")
...     .assign(date=lambda x: pd.to_datetime(x["date"]))
...     .set_index("date")
... )
>>> result = cp.InterruptedTimeSeries(
...     df,
...     treatment_time=pd.Timestamp("2017-01-01"),
...     treatment_end_time=pd.Timestamp("2017-06-01"),
...     formula="y ~ 1 + t + C(month)",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={"random_seed": 42, "progressbar": False}
...     ),
... )
>>> persistence = result.analyze_persistence()
... # Note: Results are automatically printed to console
>>> persistence["persistence_ratio"]
-1.224