SyntheticDID

Synthetic difference-in-differences for balanced panels

from _api_doc_utils import *

1 Where it fits

Group: Causal inference

SyntheticDID combines donor-unit weights and pre-period time weights. It estimates counterfactual treated outcomes by reweighting both units and periods, then reports ATT and event-time summaries under the common fit(Y, W) panel contract.

2 Python API

Constructor: cm.SyntheticDID

Use SyntheticDID(zeta_omega=None, zeta_lambda=None, max_iterations=1000). fit(y, w) infers cohorts and donors. predict(), treatment_effect(), summary(), vcov(), and se() expose fitted counterfactuals and uncertainty helpers.

print(inspect.signature(cm.SyntheticDID))
(zeta_omega=None, zeta_lambda=None, max_iterations=1000)
cls = cm.SyntheticDID
display(HTML(html_table(["Public method"], public_methods(cls))))
Public method
fit(self, /, y, w)
predict(self, /)
se(self, /, method='bootstrap', replications=200, seed=None)
summary(self, /)
treatment_effect(self, /)
vcov(self, /, method='bootstrap', replications=200, seed=None)

3 Minimal example

rng=np.random.default_rng(17)
y=rng.normal(size=(9,13)); w=np.zeros_like(y); w[6:,8:]=1; y[6:,8:]+=0.7
model=cm.SyntheticDID(max_iterations=500); model.fit(y,w)
print(model.summary()["att"])
print(model.treatment_effect().shape)
1.341684541503742
(9, 13)

4 summary() contract

The table below is generated by fitting the live class in this repository and then inspecting summary(). Shapes are shown because most values are plain NumPy arrays or scalars.

rng=np.random.default_rng(117); y=rng.normal(size=(8,12)); w=np.zeros_like(y); w[6:,8:]=1; y[6:,8:]+=.7
model=cm.SyntheticDID(max_iterations=300); model.fit(y,w)
summary = model.summary()
display(HTML(html_table(["summary() key", "shape"], summary_shape_rows(summary))))
summary() key shape
att ()
unit_weights (1, 8)
time_weights (1, 12)
counterfactual (8, 12)
synthetic_outcome (8, 12)
treatment_effect (8, 12)
event_study ()
group_means ()
pre_rmse ()
unit_intercept (1,)
time_intercept (1,)
zeta_omega (1,)
zeta_lambda (1,)
control_units (6,)
treated_units (2,)
cohorts (1,)