diff_diff.plot_honest_event_study#

diff_diff.plot_honest_event_study(honest_results, *, periods=None, reference_period=None, figsize=(10, 6), title='Event Study with Honest Confidence Intervals', xlabel='Period Relative to Treatment', ylabel='Treatment Effect', original_color='#6b7280', honest_color='#2563eb', marker='o', markersize=8, capsize=4, ax=None, show=True, backend='matplotlib')[source]

Create event study plot with Honest DiD confidence intervals.

Shows both the original confidence intervals (assuming parallel trends) and the robust confidence intervals that allow for bounded violations.

Parameters:

  • honest_results (HonestDiDResults) – Results from HonestDiD.fit() that include event_study_bounds.

  • periods (list, optional) – Periods to plot. If None, uses all available periods.

  • reference_period (any, optional) – Reference period to show as hollow marker.

  • figsize (tuple, default=(10, 6)) – Figure size.

  • title (str) – Plot title.

  • xlabel (str) – X-axis label.

  • ylabel (str) – Y-axis label.

  • original_color (str) – Color for original (standard) confidence intervals.

  • honest_color (str) – Color for honest (robust) confidence intervals.

  • marker (str) – Marker style.

  • markersize (int) – Marker size.

  • capsize (int) – Error bar cap size.

  • ax (matplotlib.axes.Axes, optional) – Axes to plot on.

  • show (bool, default=True) – Whether to call plt.show().

  • backend (str, default="matplotlib") – Plotting backend: "matplotlib" or "plotly".

Returns:

The axes object (matplotlib) or figure (plotly).

Return type:

matplotlib.axes.Axes or plotly.graph_objects.Figure

Notes

This function requires the HonestDiDResults to have been computed with event_study_bounds. If only a scalar bound was computed, use plot_sensitivity() instead.