"""Event study visualization functions."""
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
if TYPE_CHECKING:
from diff_diff.honest_did import HonestDiDResults
from diff_diff.chaisemartin_dhaultfoeuille_results import (
ChaisemartinDHaultfoeuilleResults,
)
from diff_diff.imputation import ImputationDiDResults
from diff_diff.results import MultiPeriodDiDResults
from diff_diff.stacked_did import StackedDiDResults
from diff_diff.staggered import CallawaySantAnnaResults
from diff_diff.sun_abraham import SunAbrahamResults
from diff_diff.two_stage import TwoStageDiDResults
# Type alias for results that can be plotted
PlottableResults = Union[
"MultiPeriodDiDResults",
"CallawaySantAnnaResults",
"SunAbrahamResults",
"ImputationDiDResults",
"TwoStageDiDResults",
"StackedDiDResults",
"ChaisemartinDHaultfoeuilleResults",
pd.DataFrame,
]
[docs]
def plot_event_study(
results: Optional[PlottableResults] = None,
*,
effects: Optional[Dict[Any, float]] = None,
se: Optional[Dict[Any, float]] = None,
periods: Optional[List[Any]] = None,
reference_period: Optional[Any] = None,
pre_periods: Optional[List[Any]] = None,
post_periods: Optional[List[Any]] = None,
alpha: float = 0.05,
figsize: Tuple[float, float] = (10, 6),
title: str = "Event Study",
xlabel: str = "Period Relative to Treatment",
ylabel: str = "Treatment Effect",
color: str = "#2563eb",
marker: str = "o",
markersize: int = 8,
linewidth: float = 1.5,
capsize: int = 4,
show_zero_line: bool = True,
show_reference_line: bool = True,
shade_pre: bool = True,
shade_color: str = "#f0f0f0",
ax: Optional[Any] = None,
show: bool = True,
use_cband: bool = True,
backend: str = "matplotlib",
) -> Any:
"""
Create an event study plot showing treatment effects over time.
This function creates a coefficient plot with point estimates and
confidence intervals for each time period, commonly used to visualize
dynamic treatment effects and assess pre-trends.
Parameters
----------
results : MultiPeriodDiDResults, CallawaySantAnnaResults, or DataFrame, optional
Results object from MultiPeriodDiD, CallawaySantAnna, or a DataFrame
with columns 'period', 'effect', 'se' (and optionally 'conf_int_lower',
'conf_int_upper'). If None, must provide effects and se directly.
effects : dict, optional
Dictionary mapping periods to effect estimates. Used if results is None.
se : dict, optional
Dictionary mapping periods to standard errors. Used if results is None.
periods : list, optional
List of periods to plot. If None, uses all periods from results.
reference_period : any, optional
The reference period to highlight. When explicitly provided, effects
are normalized (ref effect subtracted) and ref SE is set to NaN.
When None and auto-inferred from results, only hollow marker styling
is applied (no normalization). If None, tries to infer from results.
pre_periods : list, optional
List of pre-treatment periods. Used for shading.
post_periods : list, optional
List of post-treatment periods. Used for shading.
alpha : float, default=0.05
Significance level for confidence intervals.
figsize : tuple, default=(10, 6)
Figure size (width, height) in inches.
title : str, default="Event Study"
Plot title.
xlabel : str, default="Period Relative to Treatment"
X-axis label.
ylabel : str, default="Treatment Effect"
Y-axis label.
color : str, default="#2563eb"
Color for points and error bars.
marker : str, default="o"
Marker style for point estimates.
markersize : int, default=8
Size of markers.
linewidth : float, default=1.5
Width of error bar lines.
capsize : int, default=4
Size of error bar caps.
show_zero_line : bool, default=True
Whether to show a horizontal line at y=0.
show_reference_line : bool, default=True
Whether to show a vertical line at the reference period.
shade_pre : bool, default=True
Whether to shade the pre-treatment region.
shade_color : str, default="#f0f0f0"
Color for pre-treatment shading.
ax : matplotlib.axes.Axes, optional
Axes to plot on. If None, creates new figure.
show : bool, default=True
Whether to call plt.show() at the end.
use_cband : bool, default=True
Whether to use simultaneous confidence band CIs when available
from CallawaySantAnna results. When False, pointwise CIs from
``alpha`` are used regardless.
backend : str, default="matplotlib"
Plotting backend: ``"matplotlib"`` for static plots or
``"plotly"`` for interactive plots.
Returns
-------
matplotlib.axes.Axes or plotly.graph_objects.Figure
The axes object (matplotlib) or figure (plotly) containing the plot.
Examples
--------
Using with MultiPeriodDiD results:
>>> from diff_diff import MultiPeriodDiD, plot_event_study
>>> did = MultiPeriodDiD()
>>> results = did.fit(data, outcome='y', treatment='treated',
... time='period', post_periods=[3, 4, 5])
>>> plot_event_study(results)
Using with a DataFrame:
>>> df = pd.DataFrame({
... 'period': [-2, -1, 0, 1, 2],
... 'effect': [0.1, 0.05, 0.0, 0.5, 0.6],
... 'se': [0.1, 0.1, 0.0, 0.15, 0.15]
... })
>>> plot_event_study(df, reference_period=0)
Using with manual effects:
>>> effects = {-2: 0.1, -1: 0.05, 0: 0.0, 1: 0.5, 2: 0.6}
>>> se = {-2: 0.1, -1: 0.1, 0: 0.0, 1: 0.15, 2: 0.15}
>>> plot_event_study(effects=effects, se=se, reference_period=0)
Notes
-----
Event study plots are a standard visualization in difference-in-differences
analysis. They show:
1. **Pre-treatment periods**: Effects should be close to zero if parallel
trends holds. Large pre-treatment effects suggest the assumption may
be violated.
2. **Reference period**: Usually the last pre-treatment period (t=-1).
When explicitly specified via ``reference_period``, effects are normalized
to zero at this period. When auto-inferred, shown with hollow marker only.
3. **Post-treatment periods**: The treatment effects of interest. These
show how the outcome evolved after treatment.
The confidence intervals help assess statistical significance. Effects
whose CIs don't include zero are typically considered significant.
"""
from scipy import stats as scipy_stats
# Track if reference_period was explicitly provided by user
reference_period_explicit = reference_period is not None
# Extract data from results if provided
ci_lower_override = None
ci_upper_override = None
if results is not None:
(
effects,
se,
periods,
pre_periods,
post_periods,
reference_period,
reference_inferred,
ci_lower_override,
ci_upper_override,
) = _extract_plot_data(results, periods, pre_periods, post_periods, reference_period)
# If reference was inferred from results, it was NOT explicitly provided
if reference_inferred:
reference_period_explicit = False
# Suppress simultaneous confidence band overrides when user opts out
if not use_cband:
ci_lower_override = None
ci_upper_override = None
elif effects is None or se is None:
raise ValueError("Must provide either 'results' or both 'effects' and 'se'")
# Ensure effects and se are dicts
if not isinstance(effects, dict):
raise TypeError("effects must be a dictionary mapping periods to values")
if not isinstance(se, dict):
raise TypeError("se must be a dictionary mapping periods to values")
# Get periods to plot
if periods is None:
periods = sorted(effects.keys())
# Compute confidence intervals
critical_value = scipy_stats.norm.ppf(1 - alpha / 2)
# Normalize effects to reference period ONLY if explicitly specified by user
# Auto-inferred reference periods (from CallawaySantAnna) just get hollow marker styling,
# NO normalization. This prevents unintended normalization when the reference period
# isn't a true identifying constraint (e.g., CallawaySantAnna with base_period="varying").
if reference_period is not None and reference_period in effects and reference_period_explicit:
ref_effect = effects[reference_period]
if np.isfinite(ref_effect):
effects = {p: e - ref_effect for p, e in effects.items()}
# Set reference SE to NaN (it's now a constraint, not an estimate)
# This follows fixest convention where the omitted category has no SE/CI
se = {p: (np.nan if p == reference_period else s) for p, s in se.items()}
plot_data = []
for period in periods:
effect = effects.get(period, np.nan)
std_err = se.get(period, np.nan)
# Skip entries with NaN effect, but allow NaN SE (will plot without error bars)
if np.isnan(effect):
continue
# Use cband CI overrides when available, otherwise compute pointwise
if ci_lower_override is not None and period in ci_lower_override:
ci_lower = ci_lower_override[period]
assert ci_upper_override is not None
ci_upper = ci_upper_override[period]
elif np.isfinite(std_err):
ci_lower = effect - critical_value * std_err
ci_upper = effect + critical_value * std_err
else:
ci_lower = np.nan
ci_upper = np.nan
plot_data.append(
{
"period": period,
"effect": effect,
"se": std_err,
"ci_lower": ci_lower,
"ci_upper": ci_upper,
"is_reference": period == reference_period,
}
)
if not plot_data:
raise ValueError("No valid data to plot")
df = pd.DataFrame(plot_data)
if backend == "plotly":
return _render_event_study_plotly(
df,
reference_period=reference_period,
pre_periods=pre_periods,
title=title,
xlabel=xlabel,
ylabel=ylabel,
color=color,
marker=marker,
markersize=markersize,
shade_pre=shade_pre,
shade_color=shade_color,
show_zero_line=show_zero_line,
show_reference_line=show_reference_line,
show=show,
)
return _render_event_study_mpl(
df,
reference_period=reference_period,
pre_periods=pre_periods,
figsize=figsize,
title=title,
xlabel=xlabel,
ylabel=ylabel,
color=color,
marker=marker,
markersize=markersize,
linewidth=linewidth,
capsize=capsize,
shade_pre=shade_pre,
shade_color=shade_color,
show_zero_line=show_zero_line,
show_reference_line=show_reference_line,
ax=ax,
show=show,
)
def _render_event_study_mpl(
df,
*,
reference_period,
pre_periods,
figsize,
title,
xlabel,
ylabel,
color,
marker,
markersize,
linewidth,
capsize,
shade_pre,
shade_color,
show_zero_line,
show_reference_line,
ax,
show,
):
"""Render event study plot with matplotlib."""
from diff_diff.visualization._common import _require_matplotlib
plt = _require_matplotlib()
# Create figure if needed
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
# Convert periods to numeric for plotting
period_to_x = {p: i for i, p in enumerate(df["period"])}
x_vals = [period_to_x[p] for p in df["period"]]
# Shade pre-treatment region
if shade_pre and pre_periods is not None:
pre_x = [period_to_x[p] for p in pre_periods if p in period_to_x]
if pre_x:
ax.axvspan(min(pre_x) - 0.5, max(pre_x) + 0.5, color=shade_color, alpha=0.5, zorder=0)
# Draw horizontal zero line
if show_zero_line:
ax.axhline(y=0, color="gray", linestyle="--", linewidth=1, zorder=1)
# Draw vertical reference line
if show_reference_line and reference_period is not None:
if reference_period in period_to_x:
ref_x = period_to_x[reference_period]
ax.axvline(x=ref_x, color="gray", linestyle=":", linewidth=1, zorder=1)
# Plot error bars (only for entries with finite CI)
has_ci = df["ci_lower"].notna() & df["ci_upper"].notna()
if has_ci.any():
df_with_ci = df[has_ci]
x_with_ci = [period_to_x[p] for p in df_with_ci["period"]]
yerr = [
df_with_ci["effect"] - df_with_ci["ci_lower"],
df_with_ci["ci_upper"] - df_with_ci["effect"],
]
ax.errorbar(
x_with_ci,
df_with_ci["effect"],
yerr=yerr,
fmt="none",
color=color,
capsize=capsize,
linewidth=linewidth,
capthick=linewidth,
zorder=2,
)
# Plot point estimates
for i, row in df.iterrows():
x = period_to_x[row["period"]]
if row["is_reference"]:
# Hollow marker for reference period
ax.plot(
x,
row["effect"],
marker=marker,
markersize=markersize,
markerfacecolor="white",
markeredgecolor=color,
markeredgewidth=2,
zorder=3,
)
else:
ax.plot(x, row["effect"], marker=marker, markersize=markersize, color=color, zorder=3)
# Set labels and title
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
# Set x-axis ticks
ax.set_xticks(x_vals)
ax.set_xticklabels([str(p) for p in df["period"]])
# Add grid
ax.grid(True, alpha=0.3, axis="y")
# Tight layout
fig.tight_layout()
if show:
plt.show()
return ax
def _render_event_study_plotly(
df,
*,
reference_period,
pre_periods,
title,
xlabel,
ylabel,
color,
marker,
markersize,
shade_pre,
shade_color,
show_zero_line,
show_reference_line,
show,
):
"""Render event study plot with plotly."""
from diff_diff.visualization._common import (
_color_to_rgba,
_mpl_marker_to_plotly_symbol,
_plotly_default_layout,
_require_plotly,
)
go = _require_plotly()
fig = go.Figure()
periods = df["period"].tolist()
effects = df["effect"].tolist()
ci_lower = df["ci_lower"].tolist()
ci_upper = df["ci_upper"].tolist()
is_ref = df["is_reference"].tolist()
# Map periods to ordinal x positions (matching matplotlib renderer).
# This ensures string, timestamp, and other non-numeric periods work correctly.
period_to_x = {p: i for i, p in enumerate(periods)}
x_vals = list(range(len(periods)))
tick_labels = [str(p) for p in periods]
# Shade pre-treatment region
if shade_pre and pre_periods is not None:
pre_x = [period_to_x[p] for p in pre_periods if p in period_to_x]
if pre_x:
fig.add_vrect(
x0=min(pre_x) - 0.5,
x1=max(pre_x) + 0.5,
fillcolor=_color_to_rgba(shade_color, 0.5),
line_width=0,
layer="below",
)
# Zero line
if show_zero_line:
fig.add_hline(y=0, line_dash="dash", line_color="gray", line_width=1)
# Reference line
if show_reference_line and reference_period is not None and reference_period in period_to_x:
fig.add_vline(
x=period_to_x[reference_period], line_dash="dot", line_color="gray", line_width=1
)
# CI band (filled area)
has_ci = [not (np.isnan(lo) or np.isnan(hi)) for lo, hi in zip(ci_lower, ci_upper)]
ci_x = [period_to_x[p] for p, h in zip(periods, has_ci) if h]
ci_lo = [lo for lo, h in zip(ci_lower, has_ci) if h]
ci_hi = [hi for hi, h in zip(ci_upper, has_ci) if h]
if ci_x:
fig.add_trace(
go.Scatter(
x=ci_x + ci_x[::-1],
y=ci_hi + ci_lo[::-1],
fill="toself",
fillcolor=_color_to_rgba(color, 0.15),
line=dict(color="rgba(0,0,0,0)"),
showlegend=False,
hoverinfo="skip",
)
)
# Point estimates — separate reference vs non-reference.
# Attach original period labels via customdata + hovertemplate so hover
# shows real periods instead of ordinal positions.
non_ref_x = [period_to_x[p] for p, r in zip(periods, is_ref) if not r]
non_ref_e = [e for e, r in zip(effects, is_ref) if not r]
non_ref_labels = [str(p) for p, r in zip(periods, is_ref) if not r]
ref_x = [period_to_x[p] for p, r in zip(periods, is_ref) if r]
ref_e = [e for e, r in zip(effects, is_ref) if r]
ref_labels = [str(p) for p, r in zip(periods, is_ref) if r]
hover_tpl = "Period: %{customdata}<br>Effect: %{y:.4f}<extra></extra>"
symbol = _mpl_marker_to_plotly_symbol(marker)
if non_ref_x:
fig.add_trace(
go.Scatter(
x=non_ref_x,
y=non_ref_e,
mode="markers",
marker=dict(color=color, size=markersize, symbol=symbol),
name="Effect",
customdata=non_ref_labels,
hovertemplate=hover_tpl,
)
)
if ref_x:
fig.add_trace(
go.Scatter(
x=ref_x,
y=ref_e,
mode="markers",
marker=dict(
color="white",
size=markersize,
symbol=symbol,
line=dict(color=color, width=2),
),
name="Reference",
customdata=ref_labels,
hovertemplate=hover_tpl,
)
)
# Set tick labels to show original period values
fig.update_xaxes(tickvals=x_vals, ticktext=tick_labels)
_plotly_default_layout(fig, title=title, xlabel=xlabel, ylabel=ylabel)
if show:
fig.show()
return fig
def _extract_plot_data(
results: PlottableResults,
periods: Optional[List[Any]],
pre_periods: Optional[List[Any]],
post_periods: Optional[List[Any]],
reference_period: Optional[Any],
) -> Tuple[Dict, Dict, List, List, List, Any, bool, Optional[Dict], Optional[Dict]]:
"""
Extract plotting data from various result types.
Returns
-------
effects : dict
Mapping of period to effect estimate.
se : dict
Mapping of period to standard error.
periods : list
Ordered list of periods to plot.
pre_periods : list
Pre-treatment periods.
post_periods : list
Post-treatment periods.
reference_period : any
The reference period (explicit or inferred).
reference_inferred : bool
True if reference_period was auto-detected from results
rather than explicitly provided by the user.
ci_lower_override : dict or None
Simultaneous confidence band lower bounds, if available.
ci_upper_override : dict or None
Simultaneous confidence band upper bounds, if available.
"""
# Handle DataFrame input
if isinstance(results, pd.DataFrame):
if "period" not in results.columns:
raise ValueError("DataFrame must have 'period' column")
if "effect" not in results.columns:
raise ValueError("DataFrame must have 'effect' column")
if "se" not in results.columns:
raise ValueError("DataFrame must have 'se' column")
effects = dict(zip(results["period"], results["effect"]))
se = dict(zip(results["period"], results["se"]))
if periods is None:
periods = list(results["period"])
# Extract simultaneous confidence bands if present and finite
ci_lower_override = None
ci_upper_override = None
if "cband_lower" in results.columns and "cband_upper" in results.columns:
finite_mask = results["cband_lower"].notna() & results["cband_upper"].notna()
if finite_mask.any():
finite_rows = results[finite_mask]
ci_lower_override = dict(zip(finite_rows["period"], finite_rows["cband_lower"]))
ci_upper_override = dict(zip(finite_rows["period"], finite_rows["cband_upper"]))
# DataFrame input: reference_period was already set by caller, never inferred here
return (
effects,
se,
periods,
pre_periods,
post_periods,
reference_period,
False,
ci_lower_override,
ci_upper_override,
)
# Handle MultiPeriodDiDResults
if hasattr(results, "period_effects"):
effects = {}
se = {}
for period, pe in results.period_effects.items():
effects[period] = pe.effect
se[period] = pe.se
if pre_periods is None and hasattr(results, "pre_periods"):
pre_periods = results.pre_periods
if post_periods is None and hasattr(results, "post_periods"):
post_periods = results.post_periods
if periods is None:
periods = sorted(results.period_effects.keys())
# Auto-detect reference period from results if not explicitly provided
ref_inferred = False
if (
reference_period is None
and hasattr(results, "reference_period")
and results.reference_period is not None
):
reference_period = results.reference_period
ref_inferred = True
return (
effects,
se,
periods,
pre_periods,
post_periods,
reference_period,
ref_inferred,
None,
None,
)
# Handle ChaisemartinDHaultfoeuilleResults (dCDH event study)
# Must come before the generic event_study_effects branch because
# dCDH results also have event_study_effects but additionally have
# placebo_event_study with negative horizon keys.
if hasattr(results, "placebo_event_study") and hasattr(results, "L_max"):
effects = {}
se_dict: Dict = {}
ci_lower_override = {}
ci_upper_override = {}
has_cband = False
# Merge placebo horizons (negative keys)
if results.placebo_event_study:
for h, entry in results.placebo_event_study.items():
effects[h] = entry["effect"]
se_dict[h] = entry["se"]
if "cband_conf_int" in entry:
ci_lower_override[h] = entry["cband_conf_int"][0]
ci_upper_override[h] = entry["cband_conf_int"][1]
has_cband = True
# Reference period at 0
effects[0] = 0.0
se_dict[0] = float("nan")
# Positive horizons
if results.event_study_effects:
for h, entry in results.event_study_effects.items():
effects[h] = entry["effect"]
se_dict[h] = entry["se"]
if "cband_conf_int" in entry:
ci_lower_override[h] = entry["cband_conf_int"][0]
ci_upper_override[h] = entry["cband_conf_int"][1]
has_cband = True
if periods is None:
periods = sorted(effects.keys())
if pre_periods is None:
pre_periods = [p for p in periods if p < 0]
if post_periods is None:
post_periods = [p for p in periods if p > 0]
return (
effects,
se_dict,
periods,
pre_periods,
post_periods,
0, # reference_period is always 0 for dCDH
True, # inferred
ci_lower_override if has_cband else None,
ci_upper_override if has_cband else None,
)
# Handle CallawaySantAnnaResults (event study aggregation)
if hasattr(results, "event_study_effects") and results.event_study_effects is not None:
effects = {}
se = {}
ci_lower_override = {}
ci_upper_override = {}
has_cband = False
for rel_period, effect_data in results.event_study_effects.items():
effects[rel_period] = effect_data["effect"]
se[rel_period] = effect_data["se"]
# Use simultaneous CIs when available
if "cband_conf_int" in effect_data:
cband_ci = effect_data["cband_conf_int"]
ci_lower_override[rel_period] = cband_ci[0]
ci_upper_override[rel_period] = cband_ci[1]
has_cband = True
if periods is None:
periods = sorted(effects.keys())
# Track if reference_period was explicitly provided vs auto-inferred
reference_inferred = False
# Reference period is typically -1 for event study
if reference_period is None:
reference_inferred = True # We're about to infer it
# Detect reference period from n_groups=0 marker (normalization constraint)
# This handles anticipation > 0 where reference is at e = -1 - anticipation
for period, effect_data in results.event_study_effects.items():
if effect_data.get("n_groups", 1) == 0 or effect_data.get("n_obs", 1) == 0:
reference_period = period
break
# Fallback to -1 if no marker found (backward compatibility)
if reference_period is None:
reference_period = -1
if pre_periods is None:
pre_periods = [p for p in periods if p < 0]
if post_periods is None:
post_periods = [p for p in periods if p >= 0]
return (
effects,
se,
periods,
pre_periods,
post_periods,
reference_period,
reference_inferred,
ci_lower_override if has_cband else None,
ci_upper_override if has_cband else None,
)
raise TypeError(
f"Cannot extract plot data from {type(results).__name__}. "
"Expected MultiPeriodDiDResults, CallawaySantAnnaResults, "
"SunAbrahamResults, ImputationDiDResults, "
"ChaisemartinDHaultfoeuilleResults, or DataFrame."
)
[docs]
def plot_honest_event_study(
honest_results: "HonestDiDResults",
*,
periods: Optional[List[Any]] = None,
reference_period: Optional[Any] = None,
figsize: Tuple[float, float] = (10, 6),
title: str = "Event Study with Honest Confidence Intervals",
xlabel: str = "Period Relative to Treatment",
ylabel: str = "Treatment Effect",
original_color: str = "#6b7280",
honest_color: str = "#2563eb",
marker: str = "o",
markersize: int = 8,
capsize: int = 4,
ax: Optional[Any] = None,
show: bool = True,
backend: str = "matplotlib",
) -> Any:
"""
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
-------
matplotlib.axes.Axes or plotly.graph_objects.Figure
The axes object (matplotlib) or figure (plotly).
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.
"""
from scipy import stats as scipy_stats
# Get original results for standard CIs
original_results = honest_results.original_results
if original_results is None:
raise ValueError("HonestDiDResults must have original_results to plot event study")
# Extract data from original results
if hasattr(original_results, "period_effects"):
# MultiPeriodDiDResults
effects_dict = {p: pe.effect for p, pe in original_results.period_effects.items()}
se_dict = {p: pe.se for p, pe in original_results.period_effects.items()}
if periods is None:
periods = list(original_results.period_effects.keys())
elif hasattr(original_results, "event_study_effects"):
# CallawaySantAnnaResults
effects_dict = {
t: data["effect"] for t, data in original_results.event_study_effects.items()
}
se_dict = {t: data["se"] for t, data in original_results.event_study_effects.items()}
if periods is None:
periods = sorted(original_results.event_study_effects.keys())
else:
raise TypeError("Cannot extract event study data from original_results")
# Compute CIs
alpha_val = honest_results.alpha
z = scipy_stats.norm.ppf(1 - alpha_val / 2)
effects = [effects_dict[p] for p in periods]
original_ci_lower = [effects_dict[p] - z * se_dict[p] for p in periods]
original_ci_upper = [effects_dict[p] + z * se_dict[p] for p in periods]
# Get honest bounds if available for each period
if honest_results.event_study_bounds:
honest_ci_lower = [honest_results.event_study_bounds[p]["ci_lb"] for p in periods]
honest_ci_upper = [honest_results.event_study_bounds[p]["ci_ub"] for p in periods]
else:
# Use scalar bounds applied to all periods
honest_ci_lower = [honest_results.ci_lb] * len(periods)
honest_ci_upper = [honest_results.ci_ub] * len(periods)
if backend == "plotly":
return _render_honest_event_study_plotly(
periods=periods,
effects=effects,
original_ci_lower=original_ci_lower,
original_ci_upper=original_ci_upper,
honest_ci_lower=honest_ci_lower,
honest_ci_upper=honest_ci_upper,
honest_M=honest_results.M,
reference_period=reference_period,
title=title,
xlabel=xlabel,
ylabel=ylabel,
original_color=original_color,
honest_color=honest_color,
marker=marker,
markersize=markersize,
show=show,
)
return _render_honest_event_study_mpl(
periods=periods,
effects=effects,
original_ci_lower=original_ci_lower,
original_ci_upper=original_ci_upper,
honest_ci_lower=honest_ci_lower,
honest_ci_upper=honest_ci_upper,
honest_M=honest_results.M,
reference_period=reference_period,
figsize=figsize,
title=title,
xlabel=xlabel,
ylabel=ylabel,
original_color=original_color,
honest_color=honest_color,
marker=marker,
markersize=markersize,
capsize=capsize,
ax=ax,
show=show,
)
def _render_honest_event_study_mpl(
*,
periods,
effects,
original_ci_lower,
original_ci_upper,
honest_ci_lower,
honest_ci_upper,
honest_M,
reference_period,
figsize,
title,
xlabel,
ylabel,
original_color,
honest_color,
marker,
markersize,
capsize,
ax,
show,
):
"""Render honest event study plot with matplotlib."""
from diff_diff.visualization._common import _require_matplotlib
plt = _require_matplotlib()
# Create figure
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
x_vals = list(range(len(periods)))
# Zero line
ax.axhline(y=0, color="gray", linestyle="--", linewidth=1, alpha=0.5)
# Plot original CIs (thinner, background)
yerr_orig = [
[e - lower for e, lower in zip(effects, original_ci_lower)],
[u - e for e, u in zip(effects, original_ci_upper)],
]
ax.errorbar(
x_vals,
effects,
yerr=yerr_orig,
fmt="none",
color=original_color,
capsize=capsize - 1,
linewidth=1,
alpha=0.6,
label="Standard CI",
)
# Plot honest CIs (thicker, foreground)
yerr_honest = [
[e - lower for e, lower in zip(effects, honest_ci_lower)],
[u - e for e, u in zip(effects, honest_ci_upper)],
]
ax.errorbar(
x_vals,
effects,
yerr=yerr_honest,
fmt="none",
color=honest_color,
capsize=capsize,
linewidth=2,
label=f"Honest CI (M={honest_M:.2f})",
)
# Plot point estimates
for i, (x, effect, period) in enumerate(zip(x_vals, effects, periods)):
is_ref = period == reference_period
if is_ref:
ax.plot(
x,
effect,
marker=marker,
markersize=markersize,
markerfacecolor="white",
markeredgecolor=honest_color,
markeredgewidth=2,
zorder=3,
)
else:
ax.plot(x, effect, marker=marker, markersize=markersize, color=honest_color, zorder=3)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
ax.set_xticks(x_vals)
ax.set_xticklabels([str(p) for p in periods])
ax.legend(loc="best")
ax.grid(True, alpha=0.3, axis="y")
fig.tight_layout()
if show:
plt.show()
return ax
def _render_honest_event_study_plotly(
*,
periods,
effects,
original_ci_lower,
original_ci_upper,
honest_ci_lower,
honest_ci_upper,
honest_M,
reference_period,
title,
xlabel,
ylabel,
original_color,
honest_color,
marker,
markersize,
show,
):
"""Render honest event study plot with plotly."""
from diff_diff.visualization._common import (
_color_to_rgba,
_mpl_marker_to_plotly_symbol,
_plotly_default_layout,
_require_plotly,
)
go = _require_plotly()
fig = go.Figure()
# Zero line
fig.add_hline(y=0, line_dash="dash", line_color="gray", line_width=1, opacity=0.5)
# Original CI band
fig.add_trace(
go.Scatter(
x=list(periods) + list(periods)[::-1],
y=list(original_ci_upper) + list(original_ci_lower)[::-1],
fill="toself",
fillcolor=_color_to_rgba(original_color, 0.15),
line=dict(color="rgba(0,0,0,0)"),
name="Standard CI",
hoverinfo="skip",
)
)
# Honest CI band
fig.add_trace(
go.Scatter(
x=list(periods) + list(periods)[::-1],
y=list(honest_ci_upper) + list(honest_ci_lower)[::-1],
fill="toself",
fillcolor=_color_to_rgba(honest_color, 0.15),
line=dict(color="rgba(0,0,0,0)"),
name=f"Honest CI (M={honest_M:.2f})",
hoverinfo="skip",
)
)
# Point estimates
is_ref = [p == reference_period for p in periods]
non_ref_p = [p for p, r in zip(periods, is_ref) if not r]
non_ref_e = [e for e, r in zip(effects, is_ref) if not r]
ref_p = [p for p, r in zip(periods, is_ref) if r]
ref_e = [e for e, r in zip(effects, is_ref) if r]
symbol = _mpl_marker_to_plotly_symbol(marker)
if non_ref_p:
fig.add_trace(
go.Scatter(
x=non_ref_p,
y=non_ref_e,
mode="markers",
marker=dict(color=honest_color, size=markersize, symbol=symbol),
name="Effect",
)
)
if ref_p:
fig.add_trace(
go.Scatter(
x=ref_p,
y=ref_e,
mode="markers",
marker=dict(
color="white",
size=markersize,
symbol=symbol,
line=dict(color=honest_color, width=2),
),
name="Reference",
)
)
_plotly_default_layout(fig, title=title, xlabel=xlabel, ylabel=ylabel)
if show:
fig.show()
return fig