# `causalis.scenarios.synthetic_control.refutation.outcome_panel_plot`

## Module Contents

### Functions

`outcome_panel_plot` | Plot SCM panel outcomes over time.

### Data

`__all__` |

### API

causalis.scenarios.synthetic_control.refutation.outcome_panel_plot.outcome_panel_plot(paneldata: causalis.data_contracts.panel_data_scm.PanelDataSCM, *, show_donor_units: bool = True, donor_max_lines: Optional[int] = 20, show_donor_mean: bool = True, donor_alpha: float = 0.35, donor_linewidth: float = 1.2, shade_post_period: bool = True, figsize: Tuple[float, float] = (10.0, 5.5), dpi: int = 220, font_scale: float = 1.1, save: Optional[str] = None, save_dpi: Optional[int] = None, transparent: bool = False) → matplotlib.pyplot.Figure

  Plot SCM panel outcomes over time.

  The figure shows treated-unit outcomes over time, optional donor-unit paths, optional donor mean, and the intervention boundary.

  Parameters

  paneldata : PanelDataSCM Validated long-format panel contract. show_donor_units : bool, default True If True, draw donor trajectories. donor_max_lines : int or None, default 20 Maximum number of donor-unit lines to draw. `None` draws all donors. show_donor_mean : bool, default True If True, draw the donor-pool mean outcome path. donor_alpha : float, default 0.35 Opacity for donor-unit lines. donor_linewidth : float, default 1.2 Line width for donor-unit lines. shade_post_period : bool, default True If True, lightly shade the post-treatment region. figsize : tuple, default (10.0, 5.5) Figure size in inches. dpi : int, default 220 Dots per inch. font_scale : float, default 1.10 Font scaling factor. save : str, optional Optional path to save the figure. save_dpi : int, optional DPI for saved raster outputs. transparent : bool, default False Whether to save with a transparent background.

  Returns

  matplotlib.figure.Figure The generated figure.

causalis.scenarios.synthetic_control.refutation.outcome_panel_plot.__all__

  [‘outcome_panel_plot’]