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"""
Practitioner guidance for Difference-in-Differences analysis.
Implements Baker et al. (2025) "Difference-in-Differences Designs:
A Practitioner's Guide" as context-aware runtime guidance. Call
``practitioner_next_steps(results)`` after estimation to get a
structured set of recommended next steps.
"""
import
math
from
typing
import
Any
,
Dict
,
List
,
Optional
,
Set
# ---------------------------------------------------------------------------
# Valid step names (Baker et al. 8-step framework)
# ---------------------------------------------------------------------------
STEPS
:
Set
[
str
]
=
{
"target_parameter"
,
"assumptions"
,
"parallel_trends"
,
"estimator_selection"
,
"estimation"
,
"sensitivity"
,
"heterogeneity"
,
"robustness"
,
}
# ---------------------------------------------------------------------------
# Estimator name mapping
# ---------------------------------------------------------------------------
_ESTIMATOR_NAMES
:
Dict
[
str
,
str
]
=
{
"DiDResults"
:
"DifferenceInDifferences"
,
"MultiPeriodDiDResults"
:
"MultiPeriodDiD (Event Study)"
,
"CallawaySantAnnaResults"
:
"CallawaySantAnna"
,
"SunAbrahamResults"
:
"SunAbraham"
,
"ImputationDiDResults"
:
"ImputationDiD (Borusyak-Jaravel-Spiess)"
,
"TwoStageDiDResults"
:
"TwoStageDiD (Gardner)"
,
"StackedDiDResults"
:
"StackedDiD"
,
"SyntheticDiDResults"
:
"SyntheticDiD"
,
"TROPResults"
:
"TROP"
,
"EfficientDiDResults"
:
"EfficientDiD"
,
"ContinuousDiDResults"
:
"ContinuousDiD"
,
"TripleDifferenceResults"
:
"TripleDifference (DDD)"
,
"BaconDecompositionResults"
:
"BaconDecomposition"
,
"HeterogeneousAdoptionDiDResults"
:
"HeterogeneousAdoptionDiD (HAD)"
,
"HeterogeneousAdoptionDiDEventStudyResults"
:
"HeterogeneousAdoptionDiD (Event Study)"
,
}
# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------
def
practitioner_next_steps
(
results
:
Any
,
*
,
completed_steps
:
Optional
[
List
[
str
]]
=
None
,
verbose
:
bool
=
True
,
)
->
Dict
[
str
,
Any
]:
"""
Context-aware practitioner guidance based on Baker et al. (2025).
Inspects the type and attributes of *results* to recommend which
Baker et al. steps remain. Returns a structured dict and optionally
prints a human-readable summary.
Parameters
----------
results : Any
A diff-diff results object (e.g. ``DiDResults``,
``CallawaySantAnnaResults``, etc.).
completed_steps : list of str, optional
Steps the caller has already completed. Valid names:
``"target_parameter"``, ``"assumptions"``, ``"parallel_trends"``,
``"estimator_selection"``, ``"estimation"``, ``"sensitivity"``,
``"heterogeneity"``, ``"robustness"``.
verbose : bool, default True
If True, print a human-readable summary to stdout.
Returns
-------
dict
Keys: ``"estimator"`` (str), ``"completed"`` (list of str),
``"next_steps"`` (list of dict), ``"warnings"`` (list of str).
Each next_step dict has: ``"baker_step"`` (int), ``"label"`` (str),
``"why"`` (str), ``"code"`` (str), ``"priority"`` (str).
"""
completed
=
set
(
completed_steps
or
[])
unknown
=
completed
-
STEPS
if
unknown
:
raise
ValueError
(
f"Unknown step names:
{
unknown
}
. Valid names:
{
sorted
(
STEPS
)
}
"
)
# Estimation is always complete if we have a results object
completed
.
add
(
"estimation"
)
type_name
=
type
(
results
).
__name__
handler
=
_HANDLERS
.
get
(
type_name
,
_handle_generic
)
steps
,
warnings
=
handler
(
results
)
# Prepend Steps 1-2 (pre-estimation reasoning) to every handler's output.
# These are always relevant and filterable via completed_steps.
pre_estimation
=
[
_step
(
baker_step
=
1
,
label
=
"Define target parameter"
,
why
=
(
"State explicitly what causal effect you are estimating "
"(ATT, ATT(g,t), weighted/unweighted) and what policy "
"question it answers."
),
code
=
"# What is the target parameter? ATT? Weighted or unweighted?"
,
priority
=
"high"
,
step_name
=
"target_parameter"
,
),
_step
(
baker_step
=
2
,
label
=
"State identification assumptions"
,
why
=
(
"Name the parallel trends variant you are invoking "
"(unconditional, conditional, PT-GT-NYT, etc.), the "
"no-anticipation assumption, and any overlap conditions."
),
code
=
"# Which PT variant? No-anticipation? Overlap?"
,
priority
=
"high"
,
step_name
=
"assumptions"
,
),
]
steps
=
pre_estimation
+
steps
# Filter out completed steps
steps
=
_filter_steps
(
steps
,
completed
)
output
=
{
"estimator"
:
_ESTIMATOR_NAMES
.
get
(
type_name
,
type_name
),
"completed"
:
sorted
(
completed
),
"next_steps"
:
steps
,
"warnings"
:
warnings
,
}
if
verbose
:
_print_output
(
output
)
return
output
# ---------------------------------------------------------------------------
# Step builder helper
# ---------------------------------------------------------------------------
def
_step
(
baker_step
:
int
,
label
:
str
,
why
:
str
,
code
:
str
,
priority
:
str
=
"high"
,
step_name
:
str
=
""
,
)
->
Dict
[
str
,
Any
]:
return
{
"baker_step"
:
baker_step
,
"label"
:
label
,
"why"
:
why
,
"code"
:
code
,
"priority"
:
priority
,
"_step_name"
:
step_name
,
}
# ---------------------------------------------------------------------------
# Common steps reused across handlers
# ---------------------------------------------------------------------------
def
_parallel_trends_step
(
staggered
:
bool
=
False
)
->
Dict
[
str
,
Any
]:
if
staggered
:
return
_step
(
baker_step
=
3
,
label
=
"Test parallel trends (event-study pre-periods)"
,
why
=
(
"For staggered designs, inspect event-study pre-period "
"coefficients rather than the generic check_parallel_trends() "
"which assumes a single binary treatment with universal "
"pre-periods. Pre-treatment ATTs should be near zero. "
"Use CS with aggregate='event_study' or check the estimator's "
"event-study output directly."
),
code
=
(
"# Inspect pre-treatment event-study coefficients:
\n
"
"# (available after fitting with event-study aggregation)
\n
"
"# Pre-period effects should be near zero and insignificant."
),
step_name
=
"parallel_trends"
,
)
return
_step
(
baker_step
=
3
,
label
=
"Test parallel trends assumption"
,
why
=
(
"Parallel trends is the core identifying assumption. "
"Insignificant pre-trends do NOT prove it holds. For "
"MultiPeriodDiD or CS results, use HonestDiD to bound "
"the impact of violations."
),
code
=
(
"from diff_diff import check_parallel_trends
\n
"
"pt = check_parallel_trends(data, outcome='y', time='period',
\n
"
" treatment_group='treated')"
),
step_name
=
"parallel_trends"
,
)
def
_honest_did_step
()
->
Dict
[
str
,
Any
]:
return
_step
(
baker_step
=
6
,
label
=
"Run HonestDiD sensitivity analysis"
,
why
=
(
"Bounds the treatment effect under plausible violations of "
"parallel trends. Essential for assessing result robustness."
),
code
=
(
"from diff_diff import compute_honest_did
\n
"
"honest = compute_honest_did(results, method='relative_magnitude', M=1.0)
\n
"
"print(honest.summary())"
),
step_name
=
"sensitivity"
,
)
def
_placebo_step
()
->
Dict
[
str
,
Any
]:
"""Placebo tests for simple 2x2 DiD designs only."""
return
_step
(
baker_step
=
6
,
label
=
"Run placebo tests"
,
why
=
(
"Falsification tests using fake timing, permutation, and "
"leave-one-out diagnostics to probe assumption validity."
),
code
=
(
"from diff_diff import run_all_placebo_tests
\n
"
"# Requires binary time indicator (post=0/1), not multi-period:
\n
"
"placebo = run_all_placebo_tests(
\n
"
" data, outcome='y', treatment='treated', time='post',
\n
"
" unit='unit_id', pre_periods=[0], post_periods=[1],
\n
"
" n_permutations=500, seed=42)"
),
priority
=
"medium"
,
step_name
=
"sensitivity"
,
)
def
_robustness_compare_step
(
alternatives
:
str
)
->
Dict
[
str
,
Any
]:
return
_step
(
baker_step
=
8
,
label
=
f"Compare with alternative estimators (
{
alternatives
}
)"
,
why
=
(
"Agreement across estimators with different assumptions "
"strengthens conclusions. Disagreement reveals sensitivity."
),
code
=
(
f"# Re-estimate with
{
alternatives
}
and compare ATT, SE, CI
\n
"
f"# If results agree, confidence increases.
\n
"
f"# If they disagree, investigate which assumptions differ."
),
step_name
=
"robustness"
,
)
def
_covariates_step
()
->
Dict
[
str
,
Any
]:
return
_step
(
baker_step
=
8
,
label
=
"Report with and without covariates"
,
why
=
(
"Shows whether results are sensitive to covariate conditioning. "
"Large shifts suggest covariates are driving identification."
),
code
=
(
"# Re-estimate without covariates and compare:
\n
"
"result_no_cov = estimator.fit(data, ..., covariates=None)
\n
"
"# Compare ATT with and without covariates.
\n
"
"# Use .att (basic DiD) or .overall_att (staggered estimators)."
),
priority
=
"medium"
,
step_name
=
"robustness"
,
)
# ---------------------------------------------------------------------------
# Per-type handlers — each returns (steps, warnings)
# ---------------------------------------------------------------------------
def
_handle_did
(
results
:
Any
):
steps
=
[
_step
(
baker_step
=
3
,
label
=
"Test parallel trends assumption"
,
why
=
(
"Parallel trends is the core identifying assumption. "
"Insignificant pre-trends do NOT prove it holds."
),
code
=
(
"from diff_diff import check_parallel_trends
\n
"
"pt = check_parallel_trends(data, outcome='y', time='period',
\n
"
" treatment_group='treated')"
),
step_name
=
"parallel_trends"
,
),
_placebo_step
(),
# valid: basic 2x2 DiD with binary time
_step
(
baker_step
=
4
,
label
=
"Check if data is actually staggered"
,
why
=
(
"If treatment timing varies across units, basic DiD produces "
"biased estimates. Use CallawaySantAnna or another "
"heterogeneity-robust estimator instead."
),
code
=
(
"# Check if there are multiple treatment cohorts:
\n
"
"print(data.groupby('unit')['treatment_date'].first().nunique())
\n
"
"# If > 1 cohort, switch to CallawaySantAnna"
),
step_name
=
"estimator_selection"
,
),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_multi_period
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(),
_honest_did_step
(),
# Note: run_all_placebo_tests() requires binary time indicator,
# which MultiPeriodDiD does not use. Omit placebo for this type.
_robustness_compare_step
(
"CS, SA, or BJS"
),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_cs
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(
staggered
=
True
),
_step
(
baker_step
=
6
,
label
=
"Run HonestDiD sensitivity analysis"
,
why
=
(
"Bounds the treatment effect under plausible violations of "
"parallel trends. Requires event study effects — refit with "
"aggregate='event_study' or 'all' if not already done."
),
code
=
(
"from diff_diff import compute_honest_did
\n
"
"# CS results must have event_study_effects:
\n
"
"results = cs.fit(data, ..., aggregate='event_study')
\n
"
"honest = compute_honest_did(results, method='relative_magnitude', M=1.0)
\n
"
"print(honest.summary())"
),
step_name
=
"sensitivity"
,
),
_step
(
baker_step
=
7
,
label
=
"Examine group and event study effects"
,
why
=
(
"Aggregate ATT may mask heterogeneity across cohorts or "
"dynamic effects over time. Inspect group and event study "
"aggregations."
),
code
=
(
"# Re-fit with aggregate='all' to get all aggregations:
\n
"
"results = cs.fit(data, ..., aggregate='all')
\n
"
"print(results.group_effects) # Per-cohort ATTs
\n
"
"print(results.event_study_effects) # Dynamic effects"
),
step_name
=
"heterogeneity"
,
),
_robustness_compare_step
(
"SA, BJS, or Gardner"
),
_covariates_step
(),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_sa
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(
staggered
=
True
),
_step
(
baker_step
=
6
,
label
=
"Specification-based falsification"
,
why
=
(
"Compare results across control group definitions "
"(never_treated vs not_yet_treated) and anticipation "
"settings to assess robustness."
),
code
=
(
"# Re-estimate with different control group / anticipation:
\n
"
"# sa_alt = SunAbraham(control_group='not_yet_treated')"
),
priority
=
"medium"
,
# DR's sensitivity section runs HonestDiD, not specification
# variation; tagging this as ``sensitivity`` caused
# ``_collect_next_steps`` to suppress it after HonestDiD ran.
# Use ``specification_comparison`` so the recommendation
# persists alongside a completed HonestDiD sensitivity check.
step_name
=
"specification_comparison"
,
),
_step
(
baker_step
=
7
,
label
=
"Examine event-study and cohort effects"
,
why
=
(
"SunAbraham results include event_study_effects (dynamic "
"effects by relative period) and cohort_effects (per-cohort "
"effects). Note: SA does not have an aggregate parameter — "
"these are computed automatically during fit()."
),
code
=
(
"# SA event-study effects:
\n
"
"sa_es_df = results.to_dataframe(level='event_study')
\n
"
"# SA cohort effects:
\n
"
"sa_cohort_df = results.to_dataframe(level='cohort')"
),
step_name
=
"heterogeneity"
,
),
_robustness_compare_step
(
"CS, BJS, or Gardner"
),
_covariates_step
(),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_imputation
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(
staggered
=
True
),
_step
(
baker_step
=
6
,
label
=
"Specification-based falsification"
,
why
=
(
"ImputationDiD does not have a control_group parameter. "
"Compare results with and without covariates, vary the "
"sample (drop cohorts), and compare with CS/SA as "
"falsification checks."
),
code
=
(
"# Compare with alternative estimators as robustness:
\n
"
"# Leave-one-cohort-out sensitivity analysis"
),
priority
=
"medium"
,
# See note on SA handler: DR completes ``sensitivity`` when
# HonestDiD runs, which is unrelated to this specification-
# variation recommendation. Tag separately.
step_name
=
"specification_comparison"
,
),
_robustness_compare_step
(
"CS, SA, or Gardner"
),
_covariates_step
(),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_two_stage
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(
staggered
=
True
),
_step
(
baker_step
=
6
,
label
=
"Specification-based falsification"
,
why
=
(
"TwoStageDiD does not have a control_group parameter. "
"Compare results with and without covariates, vary the "
"sample (drop cohorts), and compare with CS/SA as "
"falsification checks."
),
code
=
(
"# Compare with alternative estimators as robustness:
\n
"
"# Leave-one-cohort-out sensitivity analysis"
),
priority
=
"medium"
,
# See note on SA handler: DR completes ``sensitivity`` when
# HonestDiD runs, which is unrelated to this specification-
# variation recommendation. Tag separately.
step_name
=
"specification_comparison"
,
),
_robustness_compare_step
(
"CS, BJS, or SA"
),
_covariates_step
(),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_stacked
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(
staggered
=
True
),
_step
(
baker_step
=
6
,
label
=
"Vary clean control definition"
,
why
=
(
"StackedDiD uses clean_control parameter (not control_group). "
"Compare results with different clean control definitions "
"and event window widths as falsification."
),
code
=
(
"# Re-estimate with different clean_control settings:
\n
"
"# stacked_alt = StackedDiD(clean_control='not_yet_treated')"
),
priority
=
"medium"
,
# See note on SA handler: DR completes ``sensitivity`` when
# HonestDiD runs, which does not replay ``clean_control``
# variation. Tag separately.
step_name
=
"specification_comparison"
,
),
_step
(
baker_step
=
7
,
label
=
"Check sub-experiment balance"
,
why
=
(
"Stacked DiD constructs sub-experiments for each cohort. "
"Verify that each sub-experiment has sufficient controls."
),
code
=
"# Check results.n_sub_experiments and inspect results.stacked_data"
,
priority
=
"medium"
,
step_name
=
"heterogeneity"
,
),
_robustness_compare_step
(
"CS, SA, or BJS"
),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_synthetic
(
results
:
Any
):
steps
=
[
_step
(
baker_step
=
6
,
label
=
"Check pre-treatment fit and weight concentration"
,
why
=
(
"Synthetic DiD relies on pre-treatment fit to construct "
"weights. Poor fit or highly concentrated unit weights "
"suggest the synthetic control may not approximate the "
"counterfactual well."
),
code
=
(
"print(f'Pre-treatment fit (RMSE): {results.pre_treatment_fit:.4f}')
\n
"
"concentration = results.get_weight_concentration()
\n
"
"print(f
\"
Effective N: {concentration['effective_n']:.1f}
\"
)
\n
"
"print(f
\"
Top-5 weight share: {concentration['top_k_share']:.2%}
\"
)"
),
step_name
=
"sensitivity"
,
),
_step
(
baker_step
=
6
,
label
=
"In-time placebo"
,
why
=
(
"Re-estimate on shifted fake treatment dates in the "
"pre-period. A credible design yields near-zero placebo "
"ATTs — departures signal that something is being picked "
"up pre-treatment, weakening the causal interpretation."
),
code
=
(
"placebo_df = results.in_time_placebo()
\n
"
"print(placebo_df)"
),
priority
=
"medium"
,
step_name
=
"sensitivity"
,
),
_step
(
baker_step
=
6
,
label
=
"Leave-one-out influence (jackknife)"
,
why
=
(
"If the estimate is driven by a single unit, robustness "
"is weak. Fit with variance_method='jackknife' and inspect "
"which units move the ATT the most."
),
code
=
(
"# Requires variance_method='jackknife' AND enough support for LOO
\n
"
"# (n_treated >= 2 and >= 2 effective-weight controls).
\n
"
"if getattr(results, '_loo_unit_ids', None) is not None:
\n
"
" loo_df = results.get_loo_effects_df()
\n
"
" print(loo_df.head(10))
\n
"
"else:
\n
"
" print('LOO not available - re-fit with '
\n
"
" 'variance_method=
\"
jackknife
\"
and ensure >=2 treated units '
\n
"
" 'with positive effective support.')"
),
priority
=
"medium"
,
# DR's SyntheticDiD native battery covers pre-treatment fit,
# weight concentration, in-time placebo, and zeta-omega
# sensitivity, but NOT the jackknife LOO workflow (which
# requires a separate ``variance_method='jackknife'`` fit
# via ``get_loo_effects_df``). Tagging this recommendation
# as ``sensitivity`` caused ``_collect_next_steps`` to
# suppress it as soon as the native block ran, even though
# the jackknife was never executed. Round-24 P2 CI review
# on PR #318; same class as round-20 Hausman mistag.
step_name
=
"loo_jackknife"
,
),
_step
(
baker_step
=
6
,
label
=
"Regularization sensitivity (zeta_omega)"
,
why
=
(
"The unit-weight regularization is auto-selected from "
"data. Show whether the ATT moves materially across a "
"grid of values to gauge robustness to this choice."
),
code
=
(
"sens_df = results.sensitivity_to_zeta_omega()
\n
"
"print(sens_df)"
),
priority
=
"low"
,
step_name
=
"sensitivity"
,
),
_step
(
baker_step
=
8
,
label
=
"Compare with staggered estimators (CS, SA)"
,
why
=
(
"SyntheticDiD is for few treated units; compare with "
"staggered estimators if applicable. Use TROP only if "
"factor confounding is suspected (different use case)."
),
code
=
(
"from diff_diff import CallawaySantAnna
\n
"
"cs = CallawaySantAnna()
\n
"
"cs_result = cs.fit(data, ...)
\n
"
"print(f'SDiD ATT: {results.att:.4f}, CS ATT: {cs_result.overall_att:.4f}')"
),
step_name
=
"robustness"
,
),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_trop
(
results
:
Any
):
steps
=
[
_step
(
baker_step
=
6
,
label
=
"Verify factor structure assumptions"
,
why
=
(
"TROP assumes an approximate factor model for untreated "
"potential outcomes. If the factor structure is misspecified, "
"estimates may be biased."
),
code
=
(
"# Check LOOCV-selected number of factors:
\n
"
"# Compare with SyntheticDiD as a robustness check"
),
step_name
=
"sensitivity"
,
),
_step
(
baker_step
=
6
,
label
=
"In-time or in-space placebo"
,
why
=
(
"Test robustness by re-estimating on a placebo treatment "
"period or dropping treated units one at a time. These "
"are the natural falsification checks for factor-model "
"panel estimators."
),
code
=
(
"# In-time placebo: re-estimate with a fake treatment date
\n
"
"# Leave-one-out: drop each treated unit and re-estimate"
),
priority
=
"medium"
,
# TROP's estimator-native diagnostics surface factor-model fit
# metrics, not in-time or in-space placebos; DR does not run
# placebos on TROP. Tag separately from ``sensitivity`` so the
# recommendation persists after DR marks the TROP native
# battery complete.
step_name
=
"placebo"
,
),
_robustness_compare_step
(
"SyntheticDiD or CS"
),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_efficient
(
results
:
Any
):
steps
=
[
_parallel_trends_step
(
staggered
=
True
),
_step
(
baker_step
=
6
,
label
=
"Compare control group definitions"
,
why
=
(
"EfficientDiD supports never_treated and last_cohort "
"control groups (not not_yet_treated). Compare results "
"across both to assess robustness."
),
code
=
(
"# Re-estimate with alternative control group:
\n
"
"# edid_alt = EfficientDiD(control_group='last_cohort')"
),
priority
=
"medium"
,
# See note on SA handler: DR completes ``sensitivity`` when
# HonestDiD runs, which does not re-estimate with an
# alternative control_group. Tag separately so this
# recommendation persists alongside a completed HonestDiD
# block.
step_name
=
"specification_comparison"
,
),
_step
(
baker_step
=
7
,
label
=
"Run Hausman pretest (PT-All vs PT-Post)"
,
why
=
(
"EfficientDiD supports both PT-All and PT-Post assumptions. "
"The Hausman pretest compares them — report which was selected."
),
code
=
(
"# Hausman pretest is a classmethod on the estimator:
\n
"
"from diff_diff import EfficientDiD
\n
"
"pretest = EfficientDiD.hausman_pretest(
\n
"
" data, outcome='y', unit='id', time='t', first_treat='g')"
),
# The Hausman pretest is a parallel-trends diagnostic per
# REGISTRY.md §EfficientDiD: it tests whether the stronger
# PT-All regime is tenable relative to PT-Post. ``DiagnosticReport``
# treats a ran Hausman block as ``parallel_trends`` completion
# (``_check_pt_hausman``), so tagging this practitioner step as
# ``parallel_trends`` keeps ``_collect_next_steps()`` from
# recommending a check the report already executed. Round-20 P2
# CI review on PR #318 flagged the earlier ``heterogeneity`` tag
# as a mismatched-step-name bug.
step_name
=
"parallel_trends"
,
),
_robustness_compare_step
(
"CS, SA, or BJS"
),
_covariates_step
(),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_continuous
(
results
:
Any
):
steps
=
[
_step
(
baker_step
=
3
,
label
=
"Assess parallel trends for continuous treatment"
,
why
=
(
"ContinuousDiD has dose-specific parallel trends assumptions "
"(PT/SPT) that differ from the binary treatment case. No "
"built-in formal test exists; inspect dose-specific "
"pre-treatment outcome trends across dose groups manually."
),
code
=
(
"# No built-in formal PT test for continuous treatment.
\n
"
"# Inspect pre-treatment outcome trends by dose group."
),
step_name
=
"parallel_trends"
,
),
_step
(
baker_step
=
4
,
label
=
"Switch to HeterogeneousAdoptionDiD if no untreated units"
,
why
=
(
"ContinuousDiD's identification assumes a never-treated "
"comparison group exists (units with dose = 0). When every "
"unit is treated at some positive dose level — a universal "
"rollout where treatment varies in intensity, not status — "
"use HeterogeneousAdoptionDiD instead. HAD identifies a "
"Weighted Average Slope (WAS) at the dose support boundary "
"by leveraging dose variation across units."
),
code
=
(
"# If your panel has no units with first_treat == 0, switch:
\n
"
"from diff_diff import HeterogeneousAdoptionDiD
\n
"
"had = HeterogeneousAdoptionDiD()
\n
"
"had_results = had.fit(
\n
"
" data, outcome_col='y', unit_col='unit',
\n
"
" time_col='t', dose_col='d', first_treat_col='first_treat')"
),
step_name
=
"estimator_selection"
,
),
_step
(
baker_step
=
7
,
label
=
"Plot dose-response curve"
,
why
=
(
"Continuous DiD estimates treatment effects at each dose "
"level. The dose-response curve reveals the functional form "
"of the treatment-dose relationship."
),
code
=
(
"from diff_diff import plot_dose_response
\n
"
"plot_dose_response(results)"
),
step_name
=
"heterogeneity"
,
),
_step
(
baker_step
=
6
,
label
=
"Check dose distribution"
,
why
=
(
"Sparse regions of the dose distribution produce imprecise "
"estimates. Verify sufficient support across dose values."
),
code
=
"# Inspect the distribution of treatment doses in your data"
,
priority
=
"medium"
,
step_name
=
"sensitivity"
,
),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_triple
(
results
:
Any
):
steps
=
[
_step
(
baker_step
=
3
,
label
=
"Assess DDD identifying assumption"
,
why
=
(
"DDD identification is weaker than requiring separate "
"parallel trends for two DiDs — it allows group-specific "
"and partition-specific PT violations as long as they "
"cancel in the triple difference. No built-in formal "
"test exists; inspect pre-treatment outcome patterns "
"across the treatment/eligibility/time cells."
),
code
=
(
"# No built-in formal DDD assumption test.
\n
"
"# Inspect pre-treatment means across treatment x eligibility
\n
"
"# cells to assess whether the DDD structure is plausible."
),
step_name
=
"parallel_trends"
,
),
_step
(
baker_step
=
7
,
label
=
"Test placebo group"
,
why
=
(
"Re-estimate using a placebo eligibility group to check "
"whether the DDD result could be an artifact of the "
"group structure rather than the treatment."
),
code
=
"# Re-estimate with a placebo eligibility group"
,
step_name
=
"heterogeneity"
,
),
_covariates_step
(),
]
warnings
=
_check_nan_att
(
results
)
return
steps
,
warnings
def
_handle_bacon
(
results
:
Any
):
steps
=
[
_step
(
baker_step
=
4
,
label
=
"Switch to heterogeneity-robust estimator"
,
why
=
(
"Bacon decomposition is diagnostic, not an estimator. "
"If substantial weight falls on 'later vs earlier' "
"comparisons, TWFE is biased. Use CS, SA, BJS, or another "
"heterogeneity-robust estimator for causal estimates."
),
code
=
(
"from diff_diff import CallawaySantAnna
\n
"
"cs = CallawaySantAnna(control_group='never_treated',
\n
"
" estimation_method='dr')
\n
"
"results = cs.fit(data, ...)"
),
step_name
=
"estimator_selection"
,
),
]
warnings
=
[]
# Check for forbidden comparisons (later vs earlier treated)
weight
=
getattr
(
results
,
"total_weight_later_vs_earlier"
,
0
)
if
isinstance
(
weight
, (
int
,
float
))
and
weight
>
0.01
:
warnings
.
append
(
f"Forbidden comparisons (later vs earlier treated) carry "
f"
{
weight
:.0%
}
of TWFE weight — TWFE estimate is contaminated. "
f"Switch to a heterogeneity-robust estimator."
)
return
steps
,
warnings
def
_handle_had
(
results
:
Any
):
"""HeterogeneousAdoptionDiD single-period guidance.
Five Baker et al. steps (3, 4, 6, 7, 8). HAD's design absence is
"no untreated unit" - comparison comes from dose variation across
units, not from an untreated holdout. Treatment varies in intensity,
not in status.
"""
steps
=
[
_step
(
baker_step
=
3
,
label
=
"Run the HAD pretest battery"
,
why
=
(
"On a two-period unweighted panel did_had_pretest_workflow "
"runs paper Section 4.2 step 1 (QUG support-infimum test - "
"decides Design 1' vs Design 1) and step 3 (Stute / "
"Yatchew-HR Assumption 8 linearity tests). Step 2 "
"(Assumption 7 pre-trends) is NOT covered on the overall "
"path - a single pre-period cannot support the joint "
"Stute variant - and the returned verdict explicitly "
"flags that gap. To close step 2, refit on a multi-period "
"panel with aggregate='event_study' AND verify the panel "
"has at least one earlier placebo pre-period beyond F-1; "
"if only the base pre-period F-1 is available, the "
"workflow still sets pretrends_joint=None, all_pass=False, "
"and a 'joint pre-trends skipped (no earlier pre-period)' "
"verdict suffix - in that case step 2 stays uncovered "
"even on the event-study path. On supported survey-weighted "
"fits (pweight + PSU/FPC under survey_design= / survey= / "
"weights=) the workflow skips QUG with a UserWarning "
"(permanent Phase 4.5 C0 deferral - extreme order statistics "
"are not smooth functionals of the empirical CDF) and returns "
"a linearity-conditional verdict only - so step 1 coverage "
"is unweighted-only and the reported verdict on supported "
"weighted fits is conditional on QUG holding by assumption. "
"Stratified (SurveyDesign(strata=...)) and replicate-weight "
"(BRR/Fay/JK1/JKn/SDR) designs raise NotImplementedError on "
"the linearity kernels and have no pretest workflow path "
"yet - deferred to a follow-up. "
"Assumptions 3 / 5 / 6 (uniform continuity at the "
"boundary, Design 1 sign / WAS_d_lower identification) "
"are NOT testable via pre-trends - the workflow vets only "
"what can be vetted."
),
code
=
(
"from diff_diff import did_had_pretest_workflow
\n
"
"report = did_had_pretest_workflow(
\n
"
" data, outcome_col='y', unit_col='unit',
\n
"
" time_col='t', dose_col='d',
\n
"
" first_treat_col='first_treat')
\n
"
"print(report.summary())
\n
"
"# verdict explicitly flags the Assumption 7 gap on the
\n
"
"# overall path; aggregate='event_study' on a multi-period
\n
"
"# panel adds joint Stute pre-trends + joint homogeneity-linearity.
\n
"
"# Passing survey_design= / weights= skips QUG (Phase 4.5 C0)
\n
"
"# and returns a linearity-conditional verdict only."
),
step_name
=
"parallel_trends"
,
),
_step
(
baker_step
=
4
,
label
=
"Confirm WAS is the target estimand (vs ATT(d) for ContinuousDiD)"
,
why
=
(
"HAD targets WAS (Weighted Average Slope) at the dose "
"support boundary. If you specifically want per-dose "
"ATT(d) / ACRT(d) dose-response curves AND your panel "
"has never-treated controls (units with first_treat == 0), "
"ContinuousDiD is the alternative — different estimand, "
"and ContinuousDiD's identification requires never-treated "
"controls. HAD itself remains valid even with a small "
"share of never-treated units (paper compatibility; see "
"REGISTRY § HeterogeneousAdoptionDiD edge cases — "
"Garrett et al. 2020 retained 12 untreated counties out "
"of 2,954). The choice is about estimand, not about "
"whether untreated units exist."
),
code
=
(
"# HAD reports WAS at the dose support boundary.
\n
"
"# If you instead want per-dose ATT(d)/ACRT(d) dose-response
\n
"
"# curves AND the panel has never-treated controls:
\n
"
"from diff_diff import ContinuousDiD
\n
"
"cdid = ContinuousDiD()
\n
"
"cdid_results = cdid.fit(
\n
"
" data, outcome='y', unit='unit', time='t',
\n
"
" first_treat='first_treat', dose='d',
\n
"
" aggregate='dose')"
),
step_name
=
"estimator_selection"
,
),
_step
(
baker_step
=
6
,
label
=
"Inspect bandwidth diagnostics (continuous designs)"
,
why
=
(
"Continuous-dose designs (continuous_at_zero / "
"continuous_near_d_lower) use an MSE-DPI bandwidth selector "
"for the bias-corrected local-linear estimator. Bandwidth "
"choice affects WAS - verify the selector landed on a "
"viable bandwidth (not boundary-clipped or near-degenerate). "
"results.bandwidth_diagnostics is None on the mass_point "
"design (parametric, no bandwidth)."
),
code
=
(
"# Inspect the auto-selected bandwidths:
\n
"
"results.bandwidth_diagnostics # None on mass_point"
),
priority
=
"medium"
,
step_name
=
"sensitivity"
,
),
_step
(
baker_step
=
7
,
label
=
"Re-fit with aggregate='event_study' for per-horizon WAS"
,
why
=
(
"On multi-period panels, the event-study aggregate returns "
"per-event-time WAS estimates instead of a single scalar. "
"Reveals whether dose response grows, decays, or stabilizes "
"across post-treatment horizons. Pre-period placebos serve "
"as a parallel-trends sanity check."
),
code
=
(
"from diff_diff import HeterogeneousAdoptionDiD
\n
"
"est = HeterogeneousAdoptionDiD()
\n
"
"es = est.fit(
\n
"
" data, outcome_col='y', unit_col='unit',
\n
"
" time_col='t', dose_col='d',
\n
"
" first_treat_col='first_treat',
\n
"
" aggregate='event_study')"
),
priority
=
"medium"
,
step_name
=
"heterogeneity"
,
),
_step
(
baker_step
=
8
,
label
=
"Verify design auto-detection with explicit design="
,
why
=
(
"design='auto' picks one of {continuous_at_zero, "
"continuous_near_d_lower, mass_point} from the dose "
"support. Re-fit with an explicit design= to verify the "
"auto-detection matched your panel structure - WAS vs "
"WAS_d_lower target parameters, and the bias-corrected "
"local-linear vs 2SLS estimation paths, differ in "
"interpretation."
),
code
=
(
"# Refit with each candidate design and compare:
\n
"
"from diff_diff import HeterogeneousAdoptionDiD
\n
"
"for d in ['continuous_at_zero', 'continuous_near_d_lower',
\n
"
" 'mass_point']:
\n
"
" try:
\n
"
" alt = HeterogeneousAdoptionDiD(design=d).fit(...)
\n
"
" print(d, alt.att, alt.target_parameter)
\n
"
" except Exception as e:
\n
"
" print(d, 'not applicable:', e)"
),
priority
=
"medium"
,
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