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"""
Visualization functions for difference-in-differences analysis.
Provides event study plots and other diagnostic visualizations.
"""
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.bacon import BaconDecompositionResults
from diff_diff.honest_did import HonestDiDResults, SensitivityResults
from diff_diff.power import PowerResults, SimulationPowerResults
from diff_diff.pretrends import PreTrendsPowerCurve, PreTrendsPowerResults
from diff_diff.results import MultiPeriodDiDResults
from diff_diff.staggered import CallawaySantAnnaResults
from diff_diff.sun_abraham import SunAbrahamResults
# Type alias for results that can be plotted
PlottableResults = Union[
"MultiPeriodDiDResults",
"CallawaySantAnnaResults",
"SunAbrahamResults",
pd.DataFrame,
]
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,
) -> 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 (normalized to effect=0). Will be shown as a
hollow marker. 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.
Returns
-------
matplotlib.axes.Axes
The axes object 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),
normalized to zero. This is the omitted category.
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.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError(
"matplotlib is required for plotting. "
"Install it with: pip install matplotlib"
)
from scipy import stats as scipy_stats
# Extract data from results if provided
if results is not None:
effects, se, periods, pre_periods, post_periods, reference_period = \
_extract_plot_data(results, periods, pre_periods, post_periods, reference_period)
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)
plot_data = []
for period in periods:
effect = effects.get(period, np.nan)
std_err = se.get(period, np.nan)
if np.isnan(effect) or np.isnan(std_err):
continue
ci_lower = effect - critical_value * std_err
ci_upper = effect + critical_value * std_err
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)
# 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
yerr = [df['effect'] - df['ci_lower'], df['ci_upper'] - df['effect']]
ax.errorbar(
x_vals, df['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 _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]:
"""
Extract plotting data from various result types.
Returns
-------
tuple
(effects, se, periods, pre_periods, post_periods, reference_period)
"""
# 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'])
return effects, se, periods, pre_periods, post_periods, reference_period
# 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 = post_periods
return effects, se, periods, pre_periods, post_periods, reference_period
# Handle CallawaySantAnnaResults (event study aggregation)
if hasattr(results, 'event_study_effects') and results.event_study_effects is not None:
effects = {}
se = {}
for rel_period, effect_data in results.event_study_effects.items():
effects[rel_period] = effect_data['effect']
se[rel_period] = effect_data['se']
if periods is None:
periods = sorted(effects.keys())
# Reference period is typically -1 for event study
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
raise TypeError(
f"Cannot extract plot data from {type(results).__name__}. "
"Expected MultiPeriodDiDResults, CallawaySantAnnaResults, "
"SunAbrahamResults, or DataFrame."
)
def plot_group_effects(
results: "CallawaySantAnnaResults",
*,
groups: Optional[List[Any]] = None,
figsize: Tuple[float, float] = (10, 6),
title: str = "Treatment Effects by Cohort",
xlabel: str = "Time Period",
ylabel: str = "Treatment Effect",
alpha: float = 0.05,
show: bool = True,
ax: Optional[Any] = None,
) -> Any:
"""
Plot treatment effects by treatment cohort (group).
Parameters
----------
results : CallawaySantAnnaResults
Results from CallawaySantAnna estimator.
groups : list, optional
List of groups (cohorts) to plot. If None, plots all groups.
figsize : tuple, default=(10, 6)
Figure size.
title : str
Plot title.
xlabel : str
X-axis label.
ylabel : str
Y-axis label.
alpha : float, default=0.05
Significance level for confidence intervals.
show : bool, default=True
Whether to call plt.show().
ax : matplotlib.axes.Axes, optional
Axes to plot on.
Returns
-------
matplotlib.axes.Axes
The axes object.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError(
"matplotlib is required for plotting. "
"Install it with: pip install matplotlib"
)
from scipy import stats as scipy_stats
if not hasattr(results, 'group_time_effects'):
raise TypeError("results must be a CallawaySantAnnaResults object")
# Get groups to plot
if groups is None:
groups = sorted(set(g for g, t in results.group_time_effects.keys()))
# Create figure
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
# Color palette
colors = plt.cm.tab10(np.linspace(0, 1, len(groups)))
critical_value = scipy_stats.norm.ppf(1 - alpha / 2)
for i, group in enumerate(groups):
# Get effects for this group
group_effects = [
(t, data) for (g, t), data in results.group_time_effects.items()
if g == group
]
group_effects.sort(key=lambda x: x[0])
if not group_effects:
continue
times = [t for t, _ in group_effects]
effects = [data['effect'] for _, data in group_effects]
ses = [data['se'] for _, data in group_effects]
yerr = [
[e - (e - critical_value * s) for e, s in zip(effects, ses)],
[(e + critical_value * s) - e for e, s in zip(effects, ses)]
]
ax.errorbar(
times, effects, yerr=yerr,
label=f'Cohort {group}', color=colors[i],
marker='o', capsize=3, linewidth=1.5
)
ax.axhline(y=0, color='gray', linestyle='--', linewidth=1)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
ax.legend(loc='best')
ax.grid(True, alpha=0.3, axis='y')
fig.tight_layout()
if show:
plt.show()
return ax
def plot_sensitivity(
sensitivity_results: "SensitivityResults",
*,
show_bounds: bool = True,
show_ci: bool = True,
breakdown_line: bool = True,
figsize: Tuple[float, float] = (10, 6),
title: str = "Honest DiD Sensitivity Analysis",
xlabel: str = "M (restriction parameter)",
ylabel: str = "Treatment Effect",
bounds_color: str = "#2563eb",
bounds_alpha: float = 0.3,
ci_color: str = "#2563eb",
ci_linewidth: float = 1.5,
breakdown_color: str = "#dc2626",
original_color: str = "#1f2937",
ax: Optional[Any] = None,
show: bool = True,
) -> Any:
"""
Plot sensitivity analysis results from Honest DiD.
Shows how treatment effect bounds and confidence intervals
change as the restriction parameter M varies.
Parameters
----------
sensitivity_results : SensitivityResults
Results from HonestDiD.sensitivity_analysis().
show_bounds : bool, default=True
Whether to show the identified set bounds as shaded region.
show_ci : bool, default=True
Whether to show robust confidence interval lines.
breakdown_line : bool, default=True
Whether to show vertical line at breakdown value.
figsize : tuple, default=(10, 6)
Figure size (width, height) in inches.
title : str
Plot title.
xlabel : str
X-axis label.
ylabel : str
Y-axis label.
bounds_color : str
Color for identified set shading.
bounds_alpha : float
Transparency for identified set shading.
ci_color : str
Color for confidence interval lines.
ci_linewidth : float
Line width for CI lines.
breakdown_color : str
Color for breakdown value line.
original_color : str
Color for original estimate line.
ax : matplotlib.axes.Axes, optional
Axes to plot on. If None, creates new figure.
show : bool, default=True
Whether to call plt.show().
Returns
-------
matplotlib.axes.Axes
The axes object containing the plot.
Examples
--------
>>> from diff_diff import MultiPeriodDiD
>>> from diff_diff.honest_did import HonestDiD
>>> from diff_diff.visualization import plot_sensitivity
>>>
>>> # Fit event study and run sensitivity analysis
>>> results = MultiPeriodDiD().fit(data, ...)
>>> honest = HonestDiD(method='relative_magnitude')
>>> sensitivity = honest.sensitivity_analysis(results)
>>>
>>> # Create sensitivity plot
>>> plot_sensitivity(sensitivity)
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError(
"matplotlib is required for plotting. "
"Install it with: pip install matplotlib"
)
# Create figure if needed
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
M = sensitivity_results.M_values
bounds_arr = np.array(sensitivity_results.bounds)
ci_arr = np.array(sensitivity_results.robust_cis)
# Plot original estimate
ax.axhline(
y=sensitivity_results.original_estimate,
color=original_color,
linestyle='-',
linewidth=1.5,
label='Original estimate',
alpha=0.7
)
# Plot zero line
ax.axhline(y=0, color='gray', linestyle='--', linewidth=1, alpha=0.5)
# Plot identified set bounds
if show_bounds:
ax.fill_between(
M, bounds_arr[:, 0], bounds_arr[:, 1],
alpha=bounds_alpha,
color=bounds_color,
label='Identified set'
)
# Plot confidence intervals
if show_ci:
ax.plot(
M, ci_arr[:, 0],
color=ci_color,
linewidth=ci_linewidth,
label='Robust CI'
)
ax.plot(
M, ci_arr[:, 1],
color=ci_color,
linewidth=ci_linewidth
)
# Plot breakdown line
if breakdown_line and sensitivity_results.breakdown_M is not None:
ax.axvline(
x=sensitivity_results.breakdown_M,
color=breakdown_color,
linestyle=':',
linewidth=2,
label=f'Breakdown (M={sensitivity_results.breakdown_M:.2f})'
)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_title(title)
ax.legend(loc='best')
ax.grid(True, alpha=0.3)
fig.tight_layout()
if show:
plt.show()
return ax
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,
) -> 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().
Returns
-------
matplotlib.axes.Axes
The axes object.
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.
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError(
"matplotlib is required for plotting. "
"Install it with: pip install matplotlib"
)
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")
# Create figure
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
# Compute CIs
alpha = honest_results.alpha
z = scipy_stats.norm.ppf(1 - alpha / 2)
x_vals = list(range(len(periods)))
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)
# 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_results.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 plot_bacon(
results: "BaconDecompositionResults",
*,
plot_type: str = "scatter",
figsize: Tuple[float, float] = (10, 6),
title: Optional[str] = None,
xlabel: str = "2x2 DiD Estimate",
ylabel: str = "Weight",
colors: Optional[Dict[str, str]] = None,
marker: str = "o",
markersize: int = 80,
alpha: float = 0.7,
show_weighted_avg: bool = True,
show_twfe_line: bool = True,
ax: Optional[Any] = None,
show: bool = True,
) -> Any:
"""
Visualize Goodman-Bacon decomposition results.
Creates either a scatter plot showing the weight and estimate for each
2x2 comparison, or a stacked bar chart showing total weight by comparison
type.
Parameters
----------
results : BaconDecompositionResults
Results from BaconDecomposition.fit() or bacon_decompose().
plot_type : str, default="scatter"
Type of plot to create:
- "scatter": Scatter plot with estimates on x-axis, weights on y-axis
- "bar": Stacked bar chart of weights by comparison type
figsize : tuple, default=(10, 6)
Figure size (width, height) in inches.
title : str, optional
Plot title. If None, uses a default based on plot_type.
xlabel : str, default="2x2 DiD Estimate"
X-axis label (scatter plot only).
ylabel : str, default="Weight"
Y-axis label.
colors : dict, optional
Dictionary mapping comparison types to colors. Keys are:
"treated_vs_never", "earlier_vs_later", "later_vs_earlier".
If None, uses default colors.
marker : str, default="o"
Marker style for scatter plot.
markersize : int, default=80
Marker size for scatter plot.
alpha : float, default=0.7
Transparency for markers/bars.
show_weighted_avg : bool, default=True
Whether to show weighted average lines for each comparison type
(scatter plot only).
show_twfe_line : bool, default=True
Whether to show a vertical line at the TWFE estimate (scatter plot only).
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.
Returns
-------
matplotlib.axes.Axes
The axes object containing the plot.
Examples
--------
Scatter plot (default):
>>> from diff_diff import bacon_decompose, plot_bacon
>>> results = bacon_decompose(data, outcome='y', unit='id',
... time='t', first_treat='first_treat')
>>> plot_bacon(results)
Bar chart of weights by type:
>>> plot_bacon(results, plot_type='bar')
Customized scatter plot:
>>> plot_bacon(results,
... colors={'treated_vs_never': 'green',
... 'earlier_vs_later': 'blue',
... 'later_vs_earlier': 'red'},
... title='My Bacon Decomposition')
Notes
-----
The scatter plot is particularly useful for understanding:
1. **Distribution of estimates**: Are 2x2 estimates clustered or spread?
Wide spread suggests heterogeneous treatment effects.
2. **Weight concentration**: Do a few comparisons dominate the TWFE?
Points with high weights have more influence.
3. **Forbidden comparison problem**: Red points (later_vs_earlier) show
comparisons using already-treated units as controls. If these have
different estimates than clean comparisons, TWFE may be biased.
The bar chart provides a quick summary of how much weight falls on
each comparison type, which is useful for assessing the severity
of potential TWFE bias.
See Also
--------
bacon_decompose : Perform the decomposition
BaconDecomposition : Class-based interface
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError(
"matplotlib is required for plotting. "
"Install it with: pip install matplotlib"
)
# Default colors
if colors is None:
colors = {
"treated_vs_never": "#22c55e", # Green - clean comparison
"earlier_vs_later": "#3b82f6", # Blue - valid comparison
"later_vs_earlier": "#ef4444", # Red - forbidden comparison
}
# Default titles
if title is None:
if plot_type == "scatter":
title = "Goodman-Bacon Decomposition"
else:
title = "TWFE Weight by Comparison Type"
# Create figure if needed
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
else:
fig = ax.get_figure()
if plot_type == "scatter":
_plot_bacon_scatter(
ax, results, colors, marker, markersize, alpha,
show_weighted_avg, show_twfe_line, xlabel, ylabel, title
)
elif plot_type == "bar":
_plot_bacon_bar(ax, results, colors, alpha, ylabel, title)
else:
raise ValueError(f"Unknown plot_type: {plot_type}. Use 'scatter' or 'bar'.")
fig.tight_layout()
if show:
plt.show()
return ax
def _plot_bacon_scatter(
ax: Any,
results: "BaconDecompositionResults",
colors: Dict[str, str],
marker: str,
markersize: int,
alpha: float,
show_weighted_avg: bool,
show_twfe_line: bool,
xlabel: str,
ylabel: str,
title: str,
) -> None:
"""Create scatter plot of Bacon decomposition."""
# Separate comparisons by type
by_type: Dict[str, List[Tuple[float, float]]] = {