SyntheticControl.plot#

SyntheticControl.plot(*, round_to=None, treated_unit=None, ci_prob=0.94, hdi_prob=None, kind='ribbon', ci_kind='hdi', num_samples=50, plot_predictors=False, figsize=(7, 8), show=True, legend_kwargs=None)[source]#

Plot the synthetic control results for a specific treated unit.

Parameters:

  • round_to (int | None) – Number of decimals used to round numerical results in the figure title (e.g. the Bayesian \(R^2\)). Defaults to None, in which case 2 significant figures are used.

  • treated_unit (str | None) – Which treated unit to plot. Must be one of the names supplied via treated_units at construction time. Defaults to None, which selects the first treated unit.

  • ci_prob (float) – Probability mass of the highest density interval drawn around the posterior predictive, causal impact, and cumulative impact bands. Must be in (0, 1]. Ignored for OLS models. Defaults to HDI_PROB (currently 0.94).

  • hdi_prob (float | None) – Deprecated. Use ci_prob instead.

  • kind (Literal['ribbon', 'histogram', 'spaghetti']) – How posterior uncertainty is rendered via plot_posterior_over_x(). Defaults to "ribbon". For "spaghetti", legends use draw lines rather than a shaded band. For "histogram", uncertainty is shown as a 2D density heatmap with a mean line overlay (no ribbon patch for legends).

  • ci_kind (Literal['hdi', 'eti']) – Credible interval type when kind="ribbon". Defaults to "hdi".

  • num_samples (int) – Number of posterior draws when kind="spaghetti". Defaults to 50. Ignored for other kinds.

  • plot_predictors (bool) – Whether to overlay the donor (control) unit trajectories on the top panel. Defaults to False.

  • figsize (tuple[float, float]) – Width and height of the figure in inches, passed to matplotlib.pyplot.subplots(). Defaults to (7, 8).

  • show (bool) – Whether to automatically display the plot. Defaults to True. Set to False if you want to modify the figure before displaying it.

  • legend_kwargs (dict[str, Any] | None) – Keyword arguments to adjust legend placement and styling. Supported keys: loc, bbox_to_anchor, fontsize, frameon, title (bbox_transform is accepted alongside bbox_to_anchor). The existing legend is modified in place so that custom handles are preserved.

Returns:

  • fig (matplotlib.figure.Figure) – The figure that was created.

  • ax (list[matplotlib.axes.Axes]) – The three axes (top: predictions, middle: causal impact, bottom: cumulative impact).

Return type:

tuple[Figure, list[Axes]]