analyze_event_study

source

analyze_event_study(
    df,
    *,
    outcome,
    unit=None,
    time=None,
    cohort,
    estimator='did2s',
    cluster=None,
    pre_window=None,
    post_window=None,
    never_treated_value=0,
    title=None,
)

Estimate and plot an event study for staggered treatment adoption.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Long panel data frame.	required
outcome	str	Outcome variable name.	required
unit	str | None	Unit (cross-section) identifier. Defaults to the declared panel entity.	None
time	str | None	Time identifier. Defaults to the declared panel time.	None
cohort	str	First-treated period for each unit; never_treated_value marks never-treated units.	required
estimator	Literal['did2s', 'twfe', 'saturated', 'lpdid']	"did2s" (Gardner two-stage, the default and robust to heterogeneity), "twfe" (classic two-way fixed effects — shown for comparison, biased under heterogeneous effects), "saturated" (Sun-Abraham, one curve per cohort) or "lpdid" (local-projections DiD).	'did2s'
cluster	str | None	Cluster variable for standard errors (defaults to unit).	None
pre_window	int | None	Event-time window for "lpdid" (ignored by the other estimators).	None
post_window	int | None	Event-time window for "lpdid" (ignored by the other estimators).	None
never_treated_value	int	The value of cohort that marks never-treated units (default 0).	0
title	str | None	Optional figure title.	None

Returns

Name	Type	Description
	EventStudyResult	df (event-time path), fig (Plotly), model (fitted pyfixest object) and estimator.

Examples

import expdpy as ex
from expdpy.data import load_staggered_did, load_staggered_did_data_def

df = ex.set_labels(
    load_staggered_did(), load_staggered_did_data_def(), set_panel=True
)
# Panel is declared, so unit=/time= are resolved automatically.
ex.analyze_event_study(df, outcome="outcome", cohort="cohort").fig