SyntheticControl.plot#

SyntheticControl.plot(*, round_to=None, treated_unit=None, hdi_prob=0.94, 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.

  • hdi_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).

  • 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]]