API Reference

`CausalImpact`

from causal_impact import CausalImpact

ci = CausalImpact(data, pre_period, post_period, model_args=None, alpha=0.05)

Parameter	Type	Description
`data`	`DataFrame` or `ndarray`	First column is the response variable, remaining columns are covariates
`pre_period`	`list[str \\| int]`	`[start, end]` of the pre-intervention period
`post_period`	`list[str \\| int]`	`[start, end]` of the post-intervention period
`model_args`	`dict` or `ModelOptions`	MCMC parameters (see below)
`alpha`	`float`	Significance level for credible intervals (default: 0.05)

Method	Returns	Description
`summary(output="summary", digits=2)`	`str`	Tabular summary of causal effects. Set `output="report"` for narrative form.
`report()`	`str`	Narrative interpretation of results (shortcut for `summary(output="report")`)
`plot(metrics=None)`	`Figure`	Matplotlib figure with original/pointwise/cumulative panels. Pass a list like `["original", "cumulative"]` to select panels.

Property	Type	Description
`inferences`	`DataFrame`	Per-timestep actuals, predictions, prediction s.d., and effect intervals
`summary_stats`	`dict`	Aggregate statistics (effect mean, CI, p-value, etc.)
`posterior_inclusion_probs`	`ndarray \\| None`	Posterior inclusion probability per covariate (requires covariates)

from causal_impact import ModelOptions

opts = ModelOptions(niter=5000, seed=123)
ci = CausalImpact(data, pre_period, post_period, model_args=opts)

Parameter	Type	Default	Description
`niter`	`int`	1000	Total MCMC iterations
`nwarmup`	`int`	500	Burn-in iterations to discard
`nchains`	`int`	1	Number of MCMC chains
`seed`	`int`	0	Random seed for reproducibility
`prior_level_sd`	`float`	0.01	Prior standard deviation for the local level
`standardize_data`	`bool`	`True`	Standardize data before fitting
`expected_model_size`	`int`	2	Expected number of active covariates for spike-and-slab prior
`dynamic_regression`	`bool`	`False`	Enable time-varying regression coefficients
`state_model`	`str`	`"local_level"`	`"local_level"` or `"local_linear_trend"`
`nseasons`	`int \\| None`	`None`	Seasonal cycle count. `nseasons=1` is equivalent to no seasonal component.
`season_duration`	`int \\| None`	`None`	Duration of each seasonal block; defaults to 1 when `nseasons` is set. Requires `nseasons` to be set.

Returned by ci._results. A frozen dataclass containing all computed quantities.

Field	Type	Description
`actual`	`ndarray`	Observed y values in the post period
`point_effects`	`ndarray`	Mean effect per time point
`point_effect_lower`	`ndarray`	Lower pointwise credible interval per time point
`point_effect_upper`	`ndarray`	Upper pointwise credible interval per time point
`point_effect_mean`	`float`	Mean of point effects across time
`ci_lower`	`float`	Lower CI bound on average effect
`ci_upper`	`float`	Upper CI bound on average effect
`cumulative_effect_total`	`float`	Total cumulative effect
`relative_effect_mean`	`float`	Relative effect (effect / predicted)
`p_value`	`float`	Bayesian one-sided tail probability
`predictions_mean`	`ndarray`	Mean counterfactual prediction
`predictions_sd`	`ndarray`	Posterior standard deviation of the counterfactual prediction
`predictions_lower`	`ndarray`	Lower CI on counterfactual
`predictions_upper`	`ndarray`	Upper CI on counterfactual