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"""Changes-in-Changes (CiC) and Quantile Difference-in-Differences (QDiD) estimators.
Implements the nonlinear difference-in-differences estimators of Athey & Imbens
(2006), "Identification and Inference in Nonlinear Difference-in-Differences
Models", Econometrica 74(2), 431-497, for the canonical 2x2 design (two groups,
two periods) with continuous outcomes:
- :class:`ChangesInChanges` (alias ``CiC``): the changes-in-changes estimator.
The counterfactual second-period outcome distribution of the treated group is
``F_{Y^N,11}(y) = F_10(F_00^{-1}(F_01(y)))`` (Theorem 3.1, eq. 9); the ATT is
the plug-in eq. (36) and quantile treatment effects follow eqs. (17)-(18).
- :class:`QDiD`: the quantile DiD comparison estimator (Section 3.3). The
authors recommend CiC over QDiD (p. 447): QDiD's justifying model is not
invariant to monotone rescaling of the outcome and places testable
restrictions on the data.
Numerical conventions match the R ``qte`` package (v1.3.1, Callaway), the
project's parity target, exactly:
- CiC uses R type-1 quantiles (the paper's eq. (35)/(A.1) inf-based
ceiling-order-statistic inverse) throughout.
- QDiD uses the additive quantile-DiD form with R type-7 (linear-interpolation)
quantiles, matching ``qte::QDiD()``. This is population-equivalent to the
paper's ``k^QDID`` transformation but a different finite-sample estimator
(see the labeled Note in docs/methodology/REGISTRY.md).
- Inference is bootstrap-only (the paper's analytical influence-function
variance is deferred): panel mode resamples units (both periods travel
together); repeated cross-section mode draws a single pooled row resample.
SEs are SDs over replicates with symmetric normal-approximation intervals.
- Covariates (``covariates=`` or trailing formula terms) port qte's ``xformla``
branch exactly - the Melly-Santangelo (2015) quantile-regression pipeline in
qte's simplified form: per-cell linear quantile regressions on the fixed
99-tau grid ``seq(0.01, 0.99, 0.01)``, quantreg ``predict.rqs``
``Fhat``/``Qhat`` step-function conventions (verbatim, including their
boundary behavior; no rearrangement), per-observation conditional-rank
imputation integrated over the treated pre-period covariate distribution,
and quantile regressions refit inside every bootstrap replicate.
Scope (per docs/methodology/papers/athey-imbens-2006-review.md): 2x2 design,
continuous outcomes, numeric covariates. Deferred and documented in the
methodology registry: the full Melly-Santangelo (2015) covariate estimator
(monotonized integrated-indicator CDFs, treated-post covariate integration,
exchangeable bootstrap), discrete-outcome bounds (Section 4), analytical
standard errors (Theorems 5.1-5.7), multiple groups/periods (Section 6), and
treatment-on-the-controls (Theorem 3.2).
"""
import
warnings
from
typing
import
Any
,
Callable
,
Dict
,
List
,
Optional
,
Tuple
import
numpy
as
np
import
pandas
as
pd
from
scipy
import
stats
from
scipy
.
optimize
import
linprog
from
diff_diff
.
_base
import
BaseEstimator
from
diff_diff
.
bootstrap_utils
import
warn_bootstrap_failure_rate
from
diff_diff
.
changes_in_changes_results
import
ChangesInChangesResults
from
diff_diff
.
utils
import
(
safe_inference
,
safe_inference_batch
,
validate_binary
,
validate_covariate_names
,
validate_n_bootstrap
,
)
# Default quantile grid: qte's ``probs = seq(0.05, 0.95, 0.05)`` (19 points), pinned to
# R's EXACT seq() doubles. ``np.arange(0.05, 0.96, 0.05)`` differs from R at 5 of the 19
# indices by one ulp, and type-1 order-statistic selection is sensitive to those ulps on
# n*p integer boundaries - so the default is hardcoded rather than computed. Locked
# against the golden fixture's stored probs by tests/test_changes_in_changes_parity.py.
_DEFAULT_QUANTILES
=
np
.
array
(
[
0.05
,
0.1
,
0.15000000000000002
,
0.2
,
0.25
,
0.3
,
0.35000000000000003
,
0.4
,
0.45
,
0.5
,
0.55
,
0.6000000000000001
,
0.6500000000000001
,
0.7000000000000001
,
0.7500000000000001
,
0.8
,
0.8500000000000001
,
0.9000000000000001
,
0.95
,
]
)
# The two estimators ChangesInChanges dispatches over (row M-015). Lowercase only -
# no "CiC"/"QDiD" casing - matching the library-wide rule that a uniform name never
# carries an altered spelling. The value is the ``kind`` handed to _fit_distributional
# and is echoed back on the results container as ``method``.
_VALID_METHODS
=
(
"cic"
,
"qdid"
)
# Row M-015: the QDiD CLASS is deprecated, the QDiD METHOD is not. Emitted once per
# construction from QDiD.__init__ (set_params re-emits via BaseEstimator's transactional
# probe re-init - the documented side effect MultiPeriodDiD's and SDDD's shims also have).
# Pinned verbatim by tests/test_v4_merge_cic.py and the targeted pytest filter in
# pyproject.toml. REMOVE WITH THE CLASS at 4.0.
_QDID_DEPRECATION_MSG
=
(
"QDiD is deprecated and will be removed in 4.0; use "
"ChangesInChanges(method='qdid') instead - the same engine, so the numbers "
"are unchanged. Only the class spelling is deprecated: method='qdid' is a "
"fully supported comparison mode and emits no warning of its own. The "
"QDiDResults alias is deprecated with it."
)
# Covariate-path quantile-regression tau grid: qte hardcodes ``seq(0.01, 0.99, 0.01)``
# inside compute.CiC/compute.QDiD (99 taus, not user-configurable). Pinned to R's EXACT
# seq() doubles for the same reason as _DEFAULT_QUANTILES: natural numpy constructions
# differ at 15-25 of the 99 indices by one ulp, the Fhat rank values are drawn FROM this
# grid, and the Qhat step function has its knots ON it - exact-knot searchsorted
# comparisons are ulp-sensitive. Locked against the golden fixture's stored qr_taus by
# tests/test_changes_in_changes_parity.py.
_QR_TAU_GRID
=
np
.
array
(
[
0.01
,
0.02
,
0.03
,
0.04
,
0.05
,
0.060000000000000005
,
0.06999999999999999
,
0.08
,
0.09
,
0.09999999999999999
,
0.11
,
0.12
,
0.13
,
0.14
,
0.15000000000000002
,
0.16
,
0.17
,
0.18000000000000002
,
0.19
,
0.2
,
0.21000000000000002
,
0.22
,
0.23
,
0.24000000000000002
,
0.25
,
0.26
,
0.27
,
0.28
,
0.29000000000000004
,
0.3
,
0.31
,
0.32
,
0.33
,
0.34
,
0.35000000000000003
,
0.36000000000000004
,
0.37
,
0.38
,
0.39
,
0.4
,
0.41000000000000003
,
0.42000000000000004
,
0.43
,
0.44
,
0.45
,
0.46
,
0.47000000000000003
,
0.48000000000000004
,
0.49
,
0.5
,
0.51
,
0.52
,
0.53
,
0.54
,
0.55
,
0.56
,
0.5700000000000001
,
0.5800000000000001
,
0.59
,
0.6
,
0.61
,
0.62
,
0.63
,
0.64
,
0.65
,
0.66
,
0.67
,
0.68
,
0.6900000000000001
,
0.7000000000000001
,
0.7100000000000001
,
0.72
,
0.73
,
0.74
,
0.75
,
0.76
,
0.77
,
0.78
,
0.79
,
0.8
,
0.81
,
0.8200000000000001
,
0.8300000000000001
,
0.8400000000000001
,
0.85
,
0.86
,
0.87
,
0.88
,
0.89
,
0.9
,
0.91
,
0.92
,
0.93
,
0.9400000000000001
,
0.9500000000000001
,
0.9600000000000001
,
0.97
,
0.98
,
0.99
,
]
)
# Duplicate-share threshold above which the discrete-outcome warning fires. Library
# choice: the paper's continuous machinery (Assumption 5.1(iii)) has no finite-sample
# ties rule, and applying it to discrete data silently returns one endpoint of the
# Section 4 bounds rather than a point estimate.
_TIE_SHARE_WARN
=
0.10
# Share of treated pre-period observations outside their conditional quantile envelope
# (the span of the 99 predicted per-observation quantiles) above which the covariate
# support warning fires. ~2% outside is EXPECTED under correct specification - the
# envelope only spans taus 0.01-0.99 - so the threshold sits well above that; 10%
# signals genuine conditional support/overlap failure (Melly-Santangelo Assumption 4).
_ENVELOPE_SHARE_WARN
=
0.10
# Minimum share of finite bootstrap replicate rows required to report SEs
# (bootstrap_utils convention).
_MIN_VALID_REPLICATE_SHARE
=
0.5
_CELL_LABELS
=
{
"y00"
:
"control pre-period (treatment=0, time=0)"
,
"y01"
:
"control post-period (treatment=0, time=1)"
,
"y10"
:
"treated pre-period (treatment=1, time=0)"
,
"y11"
:
"treated post-period (treatment=1, time=1)"
,
}
# =============================================================================
# Numeric core
# =============================================================================
def
_build_cells
(
y
:
np
.
ndarray
,
g
:
np
.
ndarray
,
t
:
np
.
ndarray
,
X
:
Optional
[
np
.
ndarray
]
=
None
)
->
Optional
[
Dict
[
str
,
np
.
ndarray
]]:
"""Split outcomes into the four sorted (group, period) cells.
Returns ``None`` if any cell is empty (bootstrap replicates use this to
signal a failed draw; ``fit`` raises instead via :func:`_split_cells`).
With covariates, each ``y..`` cell gains a row-aligned ``x..`` design block
via a PAIRED stable argsort: the sorted outcome values are identical to the
no-covariate ``np.sort`` (so every sorted-y invariant is preserved), while
the ``(y_i, x_i)`` pairing the quantile-regression path depends on stays
intact. ``x11`` is stored for symmetry but unused by both estimators.
"""
cells
=
{}
for
key
, (
gv
,
tv
)
in
{
"y00"
: (
0
,
0
),
"y01"
: (
0
,
1
),
"y10"
: (
1
,
0
),
"y11"
: (
1
,
1
)}.
items
():
mask
=
(
g
==
gv
)
&
(
t
==
tv
)
cell
=
y
[
mask
]
if
cell
.
size
==
0
:
return
None
if
X
is
None
:
cells
[
key
]
=
np
.
sort
(
cell
)
else
:
order
=
np
.
argsort
(
cell
,
kind
=
"stable"
)
cells
[
key
]
=
cell
[
order
]
cells
[
"x"
+
key
[
1
:]]
=
X
[
mask
][
order
]
return
cells
def
_split_cells
(
y
:
np
.
ndarray
,
g
:
np
.
ndarray
,
t
:
np
.
ndarray
,
X
:
Optional
[
np
.
ndarray
]
=
None
)
->
Dict
[
str
,
np
.
ndarray
]:
"""Like :func:`_build_cells` but raises on empty cells (Assumption 5.1(ii))."""
for
key
, (
gv
,
tv
)
in
{
"y00"
: (
0
,
0
),
"y01"
: (
0
,
1
),
"y10"
: (
1
,
0
),
"y11"
: (
1
,
1
)}.
items
():
if
not
np
.
any
((
g
==
gv
)
&
(
t
==
tv
)):
raise
ValueError
(
f"Empty (group, period) cell: no observations in the
{
_CELL_LABELS
[
key
]
}
cell. "
"All four 2x2 cells must be non-empty (Athey-Imbens Assumption 5.1(ii))."
)
cells
=
_build_cells
(
y
,
g
,
t
,
X
)
assert
cells
is
not
None
return
cells
def
_ecdf
(
sorted_sample
:
np
.
ndarray
,
x
:
np
.
ndarray
)
->
np
.
ndarray
:
"""Empirical CDF of ``sorted_sample`` evaluated at ``x`` (eq. 34, ``<=`` semantics).
Values below the sample minimum map to 0.0; at or above the maximum to 1.0.
"""
n
=
sorted_sample
.
shape
[
0
]
return
np
.
searchsorted
(
sorted_sample
,
x
,
side
=
"right"
)
/
n
def
_quantile_type1
(
sorted_sample
:
np
.
ndarray
,
probs
:
np
.
ndarray
)
->
np
.
ndarray
:
"""R ``quantile(x, probs, type=1)`` - exact port including R's fuzz arithmetic.
This is the paper's eq. (35)/(A.1) inf-based inverse: the ceiling order
statistic ``x_(ceil(n*p))`` with ``F^{-1}(0)`` = sample minimum. R computes
``j = floor(n*p * (1 + fuzz))`` with ``fuzz = 4 * .Machine$double.eps`` and
``h = (n*p > j)``, returning the 1-based order statistic ``x[j + h]``. The
fuzz matters because ECDF-composed probabilities like ``k/n00`` can land one
ulp above an integer after multiplication by ``n01``; a naive ceil picks a
different order statistic than R there. Do not replace this with
``np.quantile(method="inverted_cdf")`` - its arithmetic differs from R's.
"""
n
=
sorted_sample
.
shape
[
0
]
p
=
np
.
clip
(
np
.
asarray
(
probs
,
dtype
=
float
),
0.0
,
1.0
)
fuzz
=
4.0
*
np
.
finfo
(
float
).
eps
nppm
=
n
*
p
j
=
np
.
floor
(
nppm
*
(
1.0
+
fuzz
)).
astype
(
np
.
int64
)
h
=
(
nppm
>
j
).
astype
(
np
.
int64
)
idx
=
np
.
clip
(
j
+
h
,
1
,
n
)
return
sorted_sample
[
idx
-
1
]
def
_quantile_type7
(
sorted_sample
:
np
.
ndarray
,
probs
:
np
.
ndarray
)
->
np
.
ndarray
:
"""R ``quantile(x, probs)`` default type-7 == numpy's default ``linear`` method."""
p
=
np
.
clip
(
np
.
asarray
(
probs
,
dtype
=
float
),
0.0
,
1.0
)
return
np
.
quantile
(
sorted_sample
,
p
,
method
=
"linear"
)
def
_rq_fit
(
y
:
np
.
ndarray
,
X
:
np
.
ndarray
,
taus
:
np
.
ndarray
)
->
Optional
[
np
.
ndarray
]:
"""Linear quantile regression via the Koenker-Bassett LP, one solve per tau.
Matches R ``quantreg::rq(y ~ X, tau=taus)`` (default Barrodale-Roberts
simplex): both solvers return an exact-vertex solution, and with a
continuous outcome the optimum is generically unique, so coefficients
agree to ~1e-13. Primal formulation: variables ``[beta (free), u+ >= 0,
u- >= 0]`` with ``X_design @ beta + u+ - u- = y`` and objective
``tau * sum(u+) + (1 - tau) * sum(u-)``, solved by HiGHS.
Returns the ``(len(taus), k+1)`` coefficient matrix (intercept first,
matching R's ``(Intercept)`` row), or ``None`` if any tau's LP fails -
bootstrap replicates turn that into a NaN row; ``fit`` raises.
"""
n
=
y
.
shape
[
0
]
X_design
=
np
.
column_stack
([
np
.
ones
(
n
),
X
])
p
=
X_design
.
shape
[
1
]
A_eq
=
np
.
hstack
([
X_design
,
np
.
eye
(
n
),
-
np
.
eye
(
n
)])
bounds
=
[(
None
,
None
)]
*
p
+
[(
0.0
,
None
)]
*
(
2
*
n
)
coefs
=
np
.
empty
((
taus
.
shape
[
0
],
p
))
for
j
,
tau
in
enumerate
(
taus
):
c
=
np
.
concatenate
([
np
.
zeros
(
p
),
np
.
full
(
n
,
tau
),
np
.
full
(
n
,
1.0
-
tau
)])
res
=
linprog
(
c
,
A_eq
=
A_eq
,
b_eq
=
y
,
bounds
=
bounds
,
method
=
"highs"
)
if
res
.
status
!=
0
or
res
.
x
is
None
or
not
np
.
all
(
np
.
isfinite
(
res
.
x
[:
p
])):
return
None
coefs
[
j
]
=
res
.
x
[:
p
]
return
coefs
def
_design_matrix
(
x_cell
:
np
.
ndarray
)
->
np
.
ndarray
:
"""Prepend the intercept column to a cell's covariate block."""
return
np
.
column_stack
([
np
.
ones
(
x_cell
.
shape
[
0
]),
x_cell
])
def
_fhat_eval
(
preds
:
np
.
ndarray
,
taus
:
np
.
ndarray
,
y
:
np
.
ndarray
)
->
np
.
ndarray
:
"""quantreg ``predict.rqs(type="Fhat", stepfun=TRUE)`` evaluated at ``y``.
Verbatim port of the R construction (per observation): sort the 99
predicted quantiles (``o = order(pred)``, stable), build
``stepfun(pred[o], taus_ext[c(1, o)])`` with ``taus_ext = c(taus[1],
taus)``, and evaluate right-continuously. The floor is ``taus[0]`` (never
0), and the convention carries a deliberate one-step lag relative to the
"natural" CDF assignment - do not "fix" it; ranks must land exactly on the
values qte produces because the downstream Qhat lookup is knot-exact.
"""
n_obs
=
y
.
shape
[
0
]
taus_ext
=
np
.
concatenate
([
taus
[:
1
],
taus
])
ranks
=
np
.
empty
(
n_obs
)
for
i
in
range
(
n_obs
):
o
=
np
.
argsort
(
preds
[
i
],
kind
=
"stable"
)
yvals
=
np
.
concatenate
([
taus_ext
[:
1
],
taus_ext
[
o
]])
ranks
[
i
]
=
yvals
[
np
.
searchsorted
(
preds
[
i
][
o
],
y
[
i
],
side
=
"right"
)]
return
ranks
def
_qhat_eval
(
preds
:
np
.
ndarray
,
taus
:
np
.
ndarray
,
ranks
:
np
.
ndarray
)
->
np
.
ndarray
:
"""quantreg ``predict.rqs(type="Qhat", stepfun=TRUE)`` evaluated at ``ranks``.
Verbatim port: ``stepfun(taus, c(pred[1], pred))`` per observation - NO
sorting of the predicted quantiles (unlike Fhat) - evaluated
right-continuously. The ranks land exactly ON the tau knots by
construction, so the ``side="right"`` exact-knot semantics is the
parity-critical detail (bit-exact against R in the smoke spike).
"""
n_obs
=
ranks
.
shape
[
0
]
out
=
np
.
empty
(
n_obs
)
for
i
in
range
(
n_obs
):
yvals
=
np
.
concatenate
([
preds
[
i
, :
1
],
preds
[
i
]])
out
[
i
]
=
yvals
[
np
.
searchsorted
(
taus
,
ranks
[
i
],
side
=
"right"
)]
return
out
def
_cic_point
(
cells
:
Dict
[
str
,
np
.
ndarray
],
quantiles
:
np
.
ndarray
)
->
Tuple
[
float
,
np
.
ndarray
,
np
.
ndarray
]:
"""CiC ATT (eq. 36) and quantile effects (eq. 18), qte::CiC() arithmetic.
Counterfactual draws: each treated pre-period outcome is ranked in the
control pre-period distribution and pushed through the type-1 quantile of
the control post-period distribution (``F_01^{-1}(F_00(y))``, eq. 15/36).
"""
ranks
=
_ecdf
(
cells
[
"y00"
],
cells
[
"y10"
])
cf
=
_quantile_type1
(
cells
[
"y01"
],
ranks
)
att
=
float
(
np
.
mean
(
cells
[
"y11"
])
-
np
.
mean
(
cf
))
cf_sorted
=
np
.
sort
(
cf
)
qte
=
_quantile_type1
(
cells
[
"y11"
],
quantiles
)
-
_quantile_type1
(
cf_sorted
,
quantiles
)
return
att
,
qte
,
cf_sorted
def
_qdid_point
(
cells
:
Dict
[
str
,
np
.
ndarray
],
quantiles
:
np
.
ndarray
)
->
Tuple
[
float
,
np
.
ndarray
]:
"""QDiD ATT and quantile effects, matching qte::QDiD() exactly.
``qte(tau) = Q7(y11,tau) - [Q7(y10,tau) + Q7(y01,tau) - Q7(y00,tau)]``. The
ATT evaluates the control-group quantile functions at the treated
pre-period's own-sample ECDF ranks with type-7 quantiles - qte 1.3.1's
formula, which deviates in finite samples from the paper's k^QDID
transformation mean (population-equivalent; see the REGISTRY Note).
"""
q1
=
_quantile_type7
(
cells
[
"y11"
],
quantiles
)
q0
=
(
_quantile_type7
(
cells
[
"y10"
],
quantiles
)
+
_quantile_type7
(
cells
[
"y01"
],
quantiles
)
-
_quantile_type7
(
cells
[
"y00"
],
quantiles
)
)
ranks
=
_ecdf
(
cells
[
"y10"
],
cells
[
"y10"
])
att
=
float
(
np
.
mean
(
cells
[
"y11"
])
-
(
np
.
mean
(
cells
[
"y10"
])
+
np
.
mean
(
_quantile_type7
(
cells
[
"y01"
],
ranks
))
-
np
.
mean
(
_quantile_type7
(
cells
[
"y00"
],
ranks
))
)
)
return
att
,
q1
-
q0
def
_cic_point_cov
(
cells
:
Dict
[
str
,
np
.
ndarray
],
quantiles
:
np
.
ndarray
)
->
Optional
[
Tuple
[
float
,
np
.
ndarray
,
np
.
ndarray
,
np
.
ndarray
]]:
"""CiC with covariates - exact port of qte::CiC()'s ``xformla`` branch.
Per-cell linear quantile regressions on the fixed 99-tau grid in the
control cells; each treated pre-period observation gets its conditional
rank ``Fhat_{00|X_i}(Y_i)`` and imputed counterfactual
``y0t_i = Qhat_{01|X_i}(rank_i)``; the counterfactual distribution is the
empirical distribution of the imputations (integration over the treated
PRE-period covariate distribution - qte's convention; Melly-Santangelo
integrate over treated-post, see the REGISTRY Note). ATT and QTEs then
follow the unconditional arithmetic with type-1 quantiles on both sides.
Returns ``(att, qte, y0t_sorted, envelope_flags)`` - the flags mark
observations outside their conditional quantile envelope for the
fit-level support diagnostic (ignored by the bootstrap) - or ``None``
when any quantile-regression LP fails.
"""
coefs00
=
_rq_fit
(
cells
[
"y00"
],
cells
[
"x00"
],
_QR_TAU_GRID
)
coefs01
=
_rq_fit
(
cells
[
"y01"
],
cells
[
"x01"
],
_QR_TAU_GRID
)
if
coefs00
is
None
or
coefs01
is
None
:
return
None
x10_design
=
_design_matrix
(
cells
[
"x10"
])
preds00
=
x10_design
@
coefs00
.
T
preds01
=
x10_design
@
coefs01
.
T
ranks
=
_fhat_eval
(
preds00
,
_QR_TAU_GRID
,
cells
[
"y10"
])
y0t
=
_qhat_eval
(
preds01
,
_QR_TAU_GRID
,
ranks
)
att
=
float
(
np
.
mean
(
cells
[
"y11"
])
-
np
.
mean
(
y0t
))
y0t_sorted
=
np
.
sort
(
y0t
)
qte
=
_quantile_type1
(
cells
[
"y11"
],
quantiles
)
-
_quantile_type1
(
y0t_sorted
,
quantiles
)
envelope_flags
=
(
cells
[
"y10"
]
<
preds00
.
min
(
axis
=
1
))
|
(
cells
[
"y10"
]
>=
preds00
.
max
(
axis
=
1
))
return
att
,
qte
,
y0t_sorted
,
envelope_flags
def
_qdid_point_cov
(
cells
:
Dict
[
str
,
np
.
ndarray
],
quantiles
:
np
.
ndarray
)
->
Optional
[
Tuple
[
float
,
np
.
ndarray
]]:
"""QDiD with covariates - exact port of qte::QDiD()'s ``xformla`` branch.
Quantile regressions in THREE cells; the conditional rank comes from the
treated pre-period cell's OWN conditional distribution, and the imputation
is additive: ``y0t_i = Y_i + Qhat_{01|X_i}(rank_i) - Qhat_{00|X_i}(rank_i)``.
Asymmetric quantile types, ported verbatim (qte wart, REGISTRY Note):
``q1`` uses R's DEFAULT type-7 on the treated post-period sample while
``q0`` is the type-1 quantile of the imputed sample (via ``quantile.ecdf``
in R, which reconstructs the sample exactly).
"""
coefs00
=
_rq_fit
(
cells
[
"y00"
],
cells
[
"x00"
],
_QR_TAU_GRID
)
coefs01
=
_rq_fit
(
cells
[
"y01"
],
cells
[
"x01"
],
_QR_TAU_GRID
)
coefs10
=
_rq_fit
(
cells
[
"y10"
],
cells
[
"x10"
],
_QR_TAU_GRID
)
if
coefs00
is
None
or
coefs01
is
None
or
coefs10
is
None
:
return
None
x10_design
=
_design_matrix
(
cells
[
"x10"
])
preds10
=
x10_design
@
coefs10
.
T
preds01
=
x10_design
@
coefs01
.
T
preds00
=
x10_design
@
coefs00
.
T
ranks
=
_fhat_eval
(
preds10
,
_QR_TAU_GRID
,
cells
[
"y10"
])
y0t
=
(
cells
[
"y10"
]
+
_qhat_eval
(
preds01
,
_QR_TAU_GRID
,
ranks
)
-
_qhat_eval
(
preds00
,
_QR_TAU_GRID
,
ranks
)
)
att
=
float
(
np
.
mean
(
cells
[
"y11"
])
-
np
.
mean
(
y0t
))
q1
=
_quantile_type7
(
cells
[
"y11"
],
quantiles
)
q0
=
_quantile_type1
(
np
.
sort
(
y0t
),
quantiles
)
return
att
,
q1
-
q0
def
_interior_range
(
cells
:
Dict
[
str
,
np
.
ndarray
])
->
Tuple
[
float
,
float
]:
"""Eq. (17) plug-in interior range for CiC quantile effects.
``q_lower = F_10(min y00)``, ``q_upper = F_10(max y00)``: quantile effects
are point-identified only inside ``(q_lower, q_upper)`` without the full
support condition (Corollary 3.1 / Theorem 5.3).
"""
y10
=
cells
[
"y10"
]
q_lower
=
float
(
_ecdf
(
y10
,
np
.
array
([
cells
[
"y00"
][
0
]]))[
0
])
q_upper
=
float
(
_ecdf
(
y10
,
np
.
array
([
cells
[
"y00"
][
-
1
]]))[
0
])
return
q_lower
,
q_upper
def
_parse_2x2_formula
(
formula
:
str
,
data
:
pd
.
DataFrame
)
->
Tuple
[
str
,
str
,
str
,
Optional
[
List
[
str
]]]:
"""Parse ``"outcome ~ treatment * time [+ covariates]"`` style 2x2 formulas.
Mirrors the DifferenceInDifferences formula grammar for the interaction
forms, with TRAILING covariate terms. Deliberate deviations from the DiD
parser: in the ``:`` form, BOTH interaction-pair members must appear as
main effects and roles come from the MAIN-EFFECT order (not the
interaction-term order) - CiC/QDiD are not symmetric in (treatment, time),
and ``treated:post`` vs ``post:treated`` must not silently swap semantics.
Leading covariates (``"y ~ x1 + treat * post"``) are unsupported and
surface as a column-not-found error on the malformed term - list
covariates after the interaction.
"""
if
"~"
not
in
formula
:
raise
ValueError
(
"Formula must contain '~' to separate outcome from predictors"
)
lhs
,
rhs
=
formula
.
split
(
"~"
,
1
)
outcome
=
lhs
.
strip
()
rhs
=
rhs
.
strip
()
covariates
:
Optional
[
List
[
str
]]
=
None
if
"*"
in
rhs
:
parts
=
[
p
.
strip
()
for
p
in
rhs
.
split
(
"*"
)]
if
len
(
parts
)
!=
2
:
raise
ValueError
(
"Currently only supports single interaction (treatment * time)"
)
treatment
,
time
=
parts
if
"+"
in
time
:
time_parts
=
[
p
.
strip
()
for
p
in
time
.
split
(
"+"
)]
time
=
time_parts
[
0
]
covariates
=
time_parts
[
1
:]
elif
":"
in
rhs
:
terms
=
[
t
.
strip
()
for
t
in
rhs
.
split
(
"+"
)]
interaction
=
None
mains
:
List
[
str
]
=
[]
for
term
in
terms
:
if
":"
in
term
:
if
interaction
is
not
None
:
raise
ValueError
(
"Formula must contain exactly one interaction term"
)
interaction
=
term
else
:
mains
.
append
(
term
)
if
interaction
is
None
:
raise
ValueError
(
"Formula must include an interaction term (treatment * time or treatment:time)"
)
pair
=
[
p
.
strip
()
for
p
in
interaction
.
split
(
":"
)]
if
len
(
pair
)
!=
2
:
raise
ValueError
(
"Interaction term must involve exactly two variables"
)
if
pair
[
0
]
==
pair
[
1
]:
raise
ValueError
(
"Interaction term must involve two distinct variables"
)
if
pair
[
0
]
not
in
mains
or
pair
[
1
]
not
in
mains
:
raise
ValueError
(
"Both variables in the interaction term must also appear as main effects "
"('outcome ~ treatment + time + treatment:time [+ covariates]'); roles are "
"taken from the main-effect order."
)
# Roles come from the MAIN-EFFECT order, not the interaction-term order;
# remaining main effects are covariates. First occurrences only, so a
# duplicated main effect cannot corrupt the role assignment.
role_mains
:
List
[
str
]
=
[]
for
m
in
mains
:
if
m
in
pair
and
m
not
in
role_mains
:
role_mains
.
append
(
m
)
treatment
,
time
=
role_mains
[
0
],
role_mains
[
1
]
extras
=
[
m
for
m
in
mains
if
m
not
in
pair
]
covariates
=
extras
if
extras
else
None
else
:
raise
ValueError
(
"Formula must include an interaction term (treatment * time or treatment:time)"
)
for
name
in
(
outcome
,
treatment
,
time
,
*
(
covariates
or
[])):
if
name
not
in
data
.
columns
:
raise
ValueError
(
f"Column '
{
name
}
' from formula not found in data"
)
return
outcome
,
treatment
,
time
,
covariates
# =============================================================================
# Diagnostics
# =============================================================================
def
_check_support
(
cells
:
Dict
[
str
,
np
.
ndarray
])
->
None
:
"""Warn on treated pre-period support outside the control pre-period range (CiC)."""
if
cells
[
"y10"
][
0
]
<
cells
[
"y00"
][
0
]
or
cells
[
"y10"
][
-
1
]
>
cells
[
"y00"
][
-
1
]:
warnings
.
warn
(
"Treated pre-period outcomes fall outside the control pre-period support "
"(Athey-Imbens Assumption 3.4 violated). The counterfactual distribution is "
"only partially identified (Corollary 3.1): quantile effects are reliable "
"only inside the reported (q_lower, q_upper) interior range, and the ATT "
"involves extrapolation at the support edges."
,
UserWarning
,
stacklevel
=
2
,
)
def
_check_conditional_support
(
envelope_flags
:
np
.
ndarray
)
->
None
:
"""Warn on conditional support failure under covariates (CiC).
``envelope_flags`` marks treated pre-period observations whose outcome
falls outside the conditional quantile envelope spanned by their 99
predicted control pre-period quantiles - exactly the observations whose
conditional rank is the extrapolated floor/ceiling plateau of the Fhat
step function. The check covers the rank cell only (control pre-period QR
at the treated observation's covariates) - a documented design choice; see
the REGISTRY Note. ~2% outside is expected under correct specification
(the envelope spans taus 0.01-0.99), hence the 10% threshold.
"""
share
=
float
(
np
.
mean
(
envelope_flags
))
if
share
>
_ENVELOPE_SHARE_WARN
:
n_out
=
int
(
np
.
count_nonzero
(
envelope_flags
))
warnings
.
warn
(
f"
{
n_out
}
of
{
envelope_flags
.
size
}
treated pre-period outcomes (
{
share
:.0%
}
) fall "
"outside their conditional quantile envelope (the span of the 99 predicted "
"control pre-period grid quantiles, taus 0.01-0.99, at their own covariates). "
"This suggests the conditional support/overlap condition "
"(Melly-Santangelo 2015, Assumption 4 - the covariate analogue of Athey-Imbens "
"Assumption 3.4) fails: conditional ranks for these observations are extrapolated "
"tail plateaus, and the counterfactual involves out-of-support extrapolation."
,
UserWarning
,
stacklevel
=
2
,
)
def
_check_ties
(
cells
:
Dict
[
str
,
np
.
ndarray
])
->
None
:
"""Warn on heavy ties (discrete-looking outcomes) in any outcome cell."""
max_share
=
0.0
for
key
in
(
"y00"
,
"y01"
,
"y10"
,
"y11"
):
cell
=
cells
[
key
]
share
=
1.0
-
np
.
unique
(
cell
).
size
/
cell
.
size
max_share
=
max
(
max_share
,
share
)
if
max_share
>
_TIE_SHARE_WARN
:
warnings
.
warn
(
f"Outcome has heavy ties (up to
{
max_share
:.0%
}
duplicate values within a "
"(group, period) cell), suggesting a discrete or mixed distribution. The "
"continuous CiC/QDiD machinery assumes continuously distributed outcomes "
"(Athey-Imbens Assumption 5.1(iii)); with discrete outcomes only bounds are "
"point-identified (Section 4, deferred) and the continuous formulas silently "
"deliver one endpoint of those bounds."
,
UserWarning
,
stacklevel
=
2
,
)
def
_check_qdid_monotonicity
(
cells
:
Dict
[
str
,
np
.
ndarray
],
quantiles
:
np
.
ndarray
)
->
None
:
"""Warn when QDiD's counterfactual quantile curve is non-monotone (footnote 21)."""
cq
=
(
_quantile_type7
(
cells
[
"y10"
],
quantiles
)
+
_quantile_type7
(
cells
[
"y01"
],
quantiles
)
-
_quantile_type7
(
cells
[
"y00"
],
quantiles
)
)
if
np
.
any
(
np
.
diff
(
cq
)
<
-
1e-12
):
warnings
.
warn
(
"QDiD's implied counterfactual quantile function is non-monotone on the "
"requested grid (Athey-Imbens footnote 21: the QDiD model places testable "
"restrictions on the data, and they appear violated here). Interpret the "
"quantile effects with caution; ChangesInChanges does not impose this "
"restriction and is the recommended estimator (p. 447)."
,
UserWarning
,
stacklevel
=
2
,
)
# =============================================================================
# Bootstrap
# =============================================================================
def
_bootstrap_replicates
(
point_fn
:
Callable
[[
Dict
[
str
,
np
.
ndarray
],
np
.
ndarray
],
Optional
[
Tuple
[
Any
, ...]]],
y
:
np
.
ndarray
,
g
:
np
.
ndarray
,
t
:
np
.
ndarray
,
unit_ids
:
Optional
[
np
.
ndarray
],
panel
:
bool
,
n_bootstrap
:
int
,
quantiles
:
np
.
ndarray
,
rng
:
np
.
random
.
Generator
,
X
:
Optional
[
np
.
ndarray
]
=
None
,
)
->
np
.
ndarray
:
"""Bootstrap replicate matrix, shape ``(n_bootstrap, 1 + K)`` (col 0 = ATT).
Resampling matches qte 1.3.1: panel mode samples unit ids with replacement
(each unit's two periods travel together); repeated cross-section mode
draws one pooled row resample of the stacked two-period data (unstratified,
so cell sizes vary across draws). Covariates travel with the resampled
rows, and the covariate point functions refit every per-cell quantile
regression inside each replicate (qte's ``bootiter`` re-runs the whole
estimator). Replicates with an empty cell - or, on the covariate path, a
failed quantile-regression LP - produce a NaN row rather than an
exception. The RNG draw sequence is identical with and without covariates
(the quantile regressions consume no randomness), preserving seed
determinism.
"""
n_cols
=
1
+
quantiles
.
shape
[
0
]
out
=
np
.
full
((
n_bootstrap
,
n_cols
),
np
.
nan
)
if
panel
:
assert
unit_ids
is
not
None
# Pre-pivot to unit-level arrays: one (y_pre, y_post, group) triple per unit.
order
=
np
.
argsort
(
unit_ids
,
kind
=
"stable"
)
uid
,
y_o
,
g_o
,
t_o
=
unit_ids
[
order
],
y
[
order
],
g
[
order
],
t
[
order
]
X_o
=
X
[
order
]
if
X
is
not
None
else
None
pre_mask
=
t_o
==
0
# Balanced panel (enforced in fit): each unit has exactly one pre and one post row.
y_pre
=
y_o
[
pre_mask
]
y_post
=
y_o
[
~
pre_mask
]
g_unit
=
g_o
[
pre_mask
]
X_pre
=
X_o
[
pre_mask
]
if
X_o
is
not
None
else
None
X_post
=
X_o
[
~
pre_mask
]
if
X_o
is
not
None
else
None
# uid is sorted, so pre/post slices align unit-by-unit.
assert
np
.
array_equal
(
uid
[
pre_mask
],
uid
[
~
pre_mask
])
n_units
=
y_pre
.
shape
[
0
]
for
b
in
range
(
n_bootstrap
):
idx
=
rng
.
integers
(
0
,
n_units
,
n_units
)
gb
=
g_unit
[
idx
]
yb
=
np
.
concatenate
([
y_pre
[
idx
],
y_post
[
idx
]])
tb
=
np
.
concatenate
([
np
.
zeros
(
n_units
),
np
.
ones
(
n_units
)])
# Stacking order matches yb: pre-period rows first, then post.
Xb
=
(
np
.
vstack
([
X_pre
[
idx
],
X_post
[
idx
]])
if
X_pre
is
not
None
and
X_post
is
not
None
else
None
)
cells
=
_build_cells
(
yb
,
np
.
concatenate
([
gb
,
gb
]),
tb
,
Xb
)
if
cells
is
None
:
continue
res_b
=
point_fn
(
cells
,
quantiles
)
if
res_b
is
None
:
continue
out
[
b
,
0
]
=
res_b
[
0
]
out
[
b
,
1
:]
=
res_b
[
1
]
else
:
n_rows
=
y
.
shape
[
0
]
for
b
in
range
(
n_bootstrap
):
idx
=
rng
.
integers
(
0
,
n_rows
,
n_rows
)
Xb
=
X
[
idx
]
if
X
is
not
None
else
None
cells
=
_build_cells
(
y
[
idx
],
g
[
idx
],
t
[
idx
],
Xb
)
if
cells
is
None
:
continue
res_b
=
point_fn
(
cells
,
quantiles
)
if
res_b
is
None
:
continue
out
[
b
,
0
]
=
res_b
[
0
]
out
[
b
,
1
:]
=
res_b
[
1
]
return
out
def
_bootstrap_inference
(
replicates
:
np
.
ndarray
,
qte_hat
:
np
.
ndarray
,
n_bootstrap
:
int
,
context
:
str
,
)
->
Tuple
[
float
,
np
.
ndarray
,
float
,
int
]:
"""SEs and the sup-t critical value from the replicate matrix (qte conventions).
Returns ``(att_se, qte_ses, sup_t_crit, n_valid)``. SEs are SDs over finite
replicate rows (R ``sd``, ddof=1); if fewer than half the rows are finite,
all SEs and the critical value are NaN (bootstrap_utils gate). The sup-t
critical value ports qte's computeSE: an IQR-based scale ``sigmahalf`` per
quantile column (type-1 quantiles; SD floored at 1e-9 as fallback when any
column IQR is zero) and the hard-coded 0.95 type-1 quantile of the sup
statistics - independent of ``alpha`` by construction (qte parity).
"""
finite_rows
=
np
.
all
(
np
.
isfinite
(
replicates
),
axis
=
1
)
n_valid
=
int
(
np
.
count_nonzero
(
finite_rows
))
warn_bootstrap_failure_rate
(
n_valid
,
n_bootstrap
,
context
)
if
n_valid
<
max
(
2
,
_MIN_VALID_REPLICATE_SHARE
*
n_bootstrap
):
k
=
replicates
.
shape
[
1
]
-
1
return
np
.
nan
,
np
.
full
(
k
,
np
.
nan
),
np
.
nan
,
n_valid
good
=
replicates
[
finite_rows
]
with
np
.
errstate
(
divide
=
"ignore"
,
invalid
=
"ignore"
):
ses
=
np
.
std
(
good
,
axis
=
0
,
ddof
=
1
)
att_se
=
float
(
ses
[
0
])
qte_ses
=
ses
[
1
:]
qte_cols
=
good
[:,
1
:]
z_iqr
=
stats
.
norm
.
ppf
(
0.75
)
-
stats
.
norm
.
ppf
(
0.25
)
q75
=
np
.
array
([
_quantile_type1
(
np
.
sort
(
col
),
np
.
array
([
0.75
]))[
0
]
for
col
in
qte_cols
.
T
])
q25
=
np
.
array
([
_quantile_type1
(
np
.
sort
(
col
),
np
.
array
([
0.25
]))[
0
]
for
col
in
qte_cols
.
T
])
sigmahalf
=
(
q75
-
q25
)
/
z_iqr
if
np
.
any
(
sigmahalf
==
0
):
sigmahalf
=
np
.
maximum
(
qte_ses
,
1e-9
)
sup_stats
=
np
.
max
(
np
.
abs
(
qte_cols
-
qte_hat
[
None
, :])
/
sigmahalf
[
None
, :],
axis
=
1
)
sup_t_crit
=
float
(
_quantile_type1
(
np
.
sort
(
sup_stats
),
np
.
array
([
0.95
]))[
0
])
return
att_se
,
qte_ses
,
sup_t_crit
,
n_valid
# =============================================================================
# Shared fit pipeline
# =============================================================================
def
_fit_distributional
(
est
:
Any
,
data
:
pd
.
DataFrame
,
outcome
:
Optional
[
str
],
treatment
:
Optional
[
str
],
time
:
Optional
[
str
],
formula
:
Optional
[
str
],
covariates
:
Optional
[
List
[
str
]],
unit
:
Optional
[
str
],
kind
:
str
,
)
->
ChangesInChangesResults
:
"""Shared fit pipeline for ChangesInChanges and QDiD (``kind`` in {"cic", "qdid"})."""
# Re-validate hyperparameters (set_params may have mutated them since __init__).
_validate_all_params
(
est
.
get_params
())
quantiles
=
np
.
sort
(
np
.
asarray
(
_DEFAULT_QUANTILES
if
est
.
quantiles
is
None
else
est
.
quantiles
,
dtype
=
float
)
)
# Derived from the INSTANCE, not from ``kind``: with method= on the merged class,
# a kind-based map would name "QDiD" in errors raised by a fit the user made
# through ChangesInChanges(method="qdid") - a class they never constructed. This
# is byte-identical to the old mapping on both 3.x surfaces (QDiD -> "QDiD",
# ChangesInChanges -> "ChangesInChanges").
estimator_name
=
type
(
est
).
__name__
# ---- column resolution -------------------------------------------------
if
formula
is
not
None
:
# Uniform strictness (deliberately stricter than DifferenceInDifferences,
# which silently lets the formula win over explicit kwargs): mixing
# formula with explicit column arguments is ambiguous - reject it.
supplied
=
[
name
for
name
,
value
in
(
(
"outcome"
,
outcome
),
(
"treatment"
,
treatment
),
(
"time"
,
time
),
(
"covariates"
,
covariates
),
)
if
value
is
not
None
]
if
supplied
:
raise
ValueError
(
"Provide either 'formula' or explicit column arguments, not both "
f"(got formula= together with
{
supplied
}
)."
)
outcome
,
treatment
,
time
,
covariates
=
_parse_2x2_formula
(
formula
,
data
)
elif
outcome
is
None
or
treatment
is
None
or
time
is
None
:
raise
ValueError
(
"Must provide either 'formula' or all of 'outcome', 'treatment', and 'time'"
)
# ---- covariate validation ------------------------------------------------
if
isinstance
(
covariates
, (
str
,
bytes
)):
# A bare string would iterate character-wise ("x1" -> ["x", "1"]) and
# could silently fit the wrong covariate set if such columns exist.
raise
ValueError
(
f"covariates must be a list of column names, got the bare string "
f"
{
covariates
!r
}
- did you mean covariates=[
{
covariates
!r
}
]?"
)
if
covariates
is
not
None
and
len
(
covariates
)
==
0
:
covariates
=
None
if
covariates
is
not
None
:
covariates
=
[
str
(
c
)
for
c
in
covariates
]
reserved
=
{
outcome
,
treatment
,
time
}
if
est
.
panel
and
unit
is
not
None
:
reserved
.
add
(
unit
)
validate_covariate_names
(
covariates
,
reserved
,
estimator
=
estimator_name
)
for
col
in
covariates
:
if
col
not
in
data
.
columns
:
raise
ValueError
(
f"Covariate column '
{
col
}
' not found in data"
)
if
not
pd
.
api
.
types
.
is_numeric_dtype
(
data
[
col
]):
raise
ValueError
(
f"Covariate column '
{
col
}
' is not numeric.
{
estimator_name
}
accepts "
"numeric covariates only - dummy-encode categorical variables first "
"(e.g. pd.get_dummies(data, columns=[...]))."
)
used_cols
=
[
outcome
,
treatment
,
time
]
+
(
covariates
or
[])
if
est
.
panel
:
if
unit
is
None
:
raise
ValueError
(
"'unit' is required when panel=True (unit identifier column)"
)
used_cols
.
append
(
unit
)
for
col
in
used_cols
:
if
col
not
in
data
.
columns
:
raise
ValueError
(
f"Column '
{
col
}
' not found in data"
)
# ---- NA handling -------------------------------------------------------
frame
=
data
[
used_cols
].
copy
()
n_before
=
len
(
frame
)
frame
=
frame
.
dropna
()
n_dropped
=
n_before
-
len
(
frame
)
if
n_dropped
>
0
:
warnings
.
warn
(
f"Dropped
{
n_dropped
}
row(s) with missing values in "
f"
{
used_cols
}
before estimation."
,
UserWarning
,
stacklevel
=
2
,
)
if
len
(
frame
)
==
0
:
raise
ValueError
(
"No observations remain after dropping missing values"
)
y_check
=
frame
[
outcome
].
to_numpy
(
dtype
=
float
)
if
not
np
.
all
(
np
.
isfinite
(
y_check
)):
n_nonfinite
=
int
(
np
.
count_nonzero
(
~
np
.
isfinite
(
y_check
)))
raise
ValueError
(
f"Outcome column '
{
outcome
}
' contains
{
n_nonfinite
}
non-finite value(s) "
"(inf/-inf). Clean or drop these observations before fitting - they would "
"silently corrupt the empirical CDFs, quantiles, and bootstrap."
)
if
covariates
is
not
None
:
x_check
=
frame
[
covariates
].
to_numpy
(
dtype
=
float
)
if
not
np
.
all
(
np
.
isfinite
(
x_check
)):
n_nonfinite
=
int
(
np
.
count_nonzero
(
~
np
.
isfinite
(
x_check
)))
raise
ValueError
(
f"Covariate column(s)
{
covariates
}
contain
{
n_nonfinite
}
non-finite "
"value(s) (inf/-inf). Clean or drop these observations before fitting - "
"they would silently corrupt the quantile regressions and bootstrap."
)
validate_binary
(
frame
[
treatment
].
to_numpy
(
dtype
=
float
),
"treatment"
)
validate_binary
(
frame
[
time
].
to_numpy
(
dtype
=
float
),
"time"
)
# ---- panel hygiene -----------------------------------------------------
unit_ids
:
Optional
[
np
.
ndarray
]
=
None
if
est
.
panel
:
if
frame
.
duplicated
(
subset
=
[
unit
,
time
]).
any
():
raise
ValueError
(
"panel=True requires at most one row per (unit, period); found duplicate "
f"('
{
unit
}
', '
{
time
}
') combinations."
)
g_nunique
=
frame
.
groupby
(
unit
)[
treatment
].
nunique
()
if
(
g_nunique
>
1
).
any
():
bad
=
g_nunique
[
g_nunique
>
1
].
index
.
tolist
()[:
5
]
raise
ValueError
(
"The treatment-group indicator must be constant within unit in the 2x2 "
f"design (it marks group membership, not treatment receipt); units with "
f"varying values include
{
bad
}
."
)
counts
=
frame
.
groupby
(
unit
)[
time
].
count
()
incomplete
=
counts
[
counts
<
2
].
index
if
len
(
incomplete
)
>
0
:
warnings
.
warn
(
f"Dropped
{
len
(
incomplete
)
}
unit(s) not observed in both periods "
"(balanced-panel requirement, matching qte's makeBalancedPanel)."
,
UserWarning
,
stacklevel
=
2
,
)
frame
=
frame
[
~
frame
[
unit
].
isin
(
incomplete
)]
if
len
(
frame
)
==
0
:
raise
ValueError
(
"No balanced units remain after panel balancing"
)
unit_ids
=
frame
[
unit
].
to_numpy
()
y
=
frame
[
outcome
].
to_numpy
(
dtype
=
float
)
g
=
frame
[
treatment
].
to_numpy
(
dtype
=
float
).
astype
(
np
.
int64
)
t
=
frame
[
time
].
to_numpy
(
dtype
=
float
).
astype
(
np
.
int64
)
# Extracted AFTER panel balancing so dropped-unit rows leave X consistently.
X
=
frame
[
covariates
].
to_numpy
(
dtype
=
float
)
if
covariates
is
not
None
else
None
# ---- cells + diagnostics -----------------------------------------------
cells
=
_split_cells
(
y
,
g
,
t
,
X
)
if
X
is
not
None
:
# Fail closed at fit: below k+2 rows the quantile-regression LP has
# exact-fit degeneracy (rq produces garbage or errors there too).
# Bootstrap replicates instead NaN-row on LP failure, matching qte,
# which has no size guard.
k
=
X
.
shape
[
1
]
qr_cells
=
(
"y00"
,
"y01"
)
if
kind
==
"cic"
else
(
"y00"
,
"y01"
,
"y10"
)
for
key
in
qr_cells
:
if
cells
[
key
].
size
<
k
+
2
:
raise
ValueError
(
f"Too few observations for quantile regression in the "
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