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method = "mc" treated missing (id, time)
panel cells and NA outcomes as observed zeros instead of
excluding them from the observation mask used by the Soft-Impute solver.
Missing cells are now masked out the same way as treated post-adoption
cells. This changes numerical results for mc fits
on unbalanced panels or panels with NA
outcomes.method = "tasc"’s EM loop already handles missing
outcome cells (Kalman smoother plus per-unit M-steps), but its
initialisation (svd(), the treated-unit loading OLS, and
var()) failed on panels with missing cells. Initial values
are now computed from a column-mean-imputed matrix; the EM loop itself
still runs on the true, unimputed data.Malformed panels previously produced confusing or misleading errors —
for example, a 0-row data frame (from an upstream filtering bug) was
misclassified as staggered adoption and failed with an unrelated
“predictors not supported” error, and missing (id, time)
cells surfaced as a raw eig_sym(): decomposition failed
from the C++ layer. panel_to_matrices() (shared by all six
estimators) and scm_design() now validate and error clearly
on:
NA unit or time identifiersNA or negative treatment indicator values(id, time) cells or non-finite outcomes, for
the estimators that require a fully observed panel (scm,
sdid, gsc, si — mc
and tasc handle missing data by design)scm_fit() also now rejects non-numeric
(factor/character) outcome or treatment
columns and non-integer treatment values, instead of silently coercing
them (as.integer(factor(...)) returns level codes, not the
original values). “All cohort-level fits failed” errors (SCM/SDID/GSC/SI
staggered paths) now point back to the preceding per-cohort warnings for
diagnosis.
solve_simplex_qp() /
solve_simplex_qp_lr()), used by every
scm_fit(method = "scm") fit and by the
method = "sdid" unit weights, now use FISTA with adaptive
restart (O’Donoghue & Candès 2015, gradient scheme). The first-order
solver’s iteration count grows with the condition number of
Q = X0'VX0, which is large for the collinear pre-treatment
outcome panels typical of synthetic control; resetting the momentum term
when it works against the gradient removes this slowdown. Each inner
solve converges to the same optimum (verified against an exact QP solver
to within 1e-10), so the returned weights are
unchanged.~1e-5). For v_selection = "oos"
and penalised (lambda_pen) fits on ill-conditioned panels,
the non-convex outer V search may now settle on a different local
optimum, shifting results slightly; both the previous and the new
solutions are valid SCM fits with the same objective. This can
change numerical results for v_selection = "oos" and
penalised fits on poorly conditioned data.panel_to_matrices() (and therefore every
scm_fit() method) and scm_design() now error
on duplicate (id, time) entries instead of silently keeping
a single arbitrary row. A balanced panel requires each unit-time cell to
be unique; duplicates were previously overwritten by the last
matrix-index assignment, dropping data without warning. The error
reports the number of offending rows and the first duplicated unit and
time.plot() now renders
Date/POSIXct time axes correctly. The time
vector was coerced with as.numeric(), so dates appeared as
days-since-epoch (e.g. 16000,
Date/POSIXct values are now
passed through unchanged so ggplot2 selects the appropriate date scale;
only character/factor time values are coerced
to numeric.v_selection = "oos" (outcomes-only case) previously fit
candidate W(V) on the full pre-treatment outcome matrix and
restricted only the MSPE evaluation to the validation window, allowing
the V optimiser to fit the validation period indirectly (a data leak
relative to Abadie (2021) S.3.2). The new
.scm_oos_outcomes() implements the correct train/validation
split: candidate W(V) are fitted on training-half outcomes
only, V* minimises validation-half MSPE, and
W* is refit with V* on the outcomes of the
last floor(T_pre/2) pre-treatment periods. For OOS fits,
v_weights now has floor(T_pre/2) entries and a
new v_rows field records which periods they refer to.
This changes numerical results for
v_selection = "oos".scale_predictors (default TRUE): predictor
rows supplied via predictors = are now divided by their
standard deviation across all units before optimisation, matching the
Synth reference implementation (Abadie, Diamond & Hainmueller 2011,
JSS). predictor_table continues to report values on the
original scale. This changes numerical results for SCM fits with
user-supplied predictors unless
scale_predictors = FALSE.placebo_in_time(): in-time placebo (backdating) test
for sharp SCM fits (Abadie, Diamond & Hainmueller 2015; Abadie &
Vives-i-Bastida 2022).loo_donors(): leave-one-out donor robustness check with
the predictor weights V held fixed (Abadie, Diamond & Hainmueller
2015, footnote 20).build_predictor_matrices() now errors with an
informative message if a pred() time window produces
missing or non-finite predictor values.conformal_inference()): permutation-based p-values and
confidence intervals following Chernozhukov, Wüthrich & Zhu (2021).
Works with sharp fits across all supported estimation methods
(scm, sdid, gsc, mc,
si). The counterfactual proxy is re-estimated under the
null on all T periods (essential for finite-sample validity per
CWZ S.2.2), and p-values are obtained via moving-block (cyclic-shift)
permutation of the estimated residuals. Confidence intervals are
constructed by test inversion over a user-supplied or automatically
chosen grid. Returns a coresynth_inference subclass
compatible with tidy() and glance().panel_to_matrices(): fill loop replaced by vectorised
match() + matrix-index assignment; removes an O(n × (T +
N)) bottleneck in the shared data-prep path.tasc.cpp: safe_inv_sympd() helper added so
the Kalman filter degrades to pinv instead of aborting when
the innovation covariance is not numerically PD.%||% null-coalescing helper centralised in
utils.R; duplicate definitions in broom.R and
plot.R removed.check_sharp_adoption() (unused internal function)
removed.First public release.
pred(), out-of-sample V selection
(v_selection = "oos"), donor filtering
(donor_mspe_threshold), penalised SCM
(lambda_pen), and staggered adoption. Inference: MSPE ratio
permutation test via mspe_ratio_pval().covariates =), sharp and staggered adoption. Inference:
sdid_inference() with placebo / bootstrap / jackknife /
jackknife_global.gsc_boot()) and non-parametric
(gsc_inference()).si_inference() with
bootstrap / jackknife / jackknife_global.scm_design() with base / weakly_targeted / unit_level
variants, blank-period permutation test, and split-conformal confidence
intervals.scm_fit(outcome ~ treatment | unit + time, data, method = ...)
entry point for all methods.panel_to_tensor() for multi-arm SI data
preparation.broom integration: tidy(),
glance(), augment() for all methods and
inference objects.plot.coresynth(): trend, gap, and weights plots via
ggplot2.export_json(): JSON export for reproducibility.All core optimisations implemented in C++ via RcppArmadillo: 50–70x
faster than the Synth package for typical panel sizes
(N_co ≤ 30). src/inference.cpp placebo loops parallelised
with OpenMP.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.