Arkhangelsky, Dmitry, et al. Synthetic difference in differences. No. w25532. National Bureau of Economic Research, 2019. https://www.nber.org/papers/w25532
https://github.com/synth-inference/synthdid
$ pip install git+https://github.com/MasaAsami/pysynthdid
This package is still under development. I plan to register with pypi
after the following specifications are met.
from synthdid.model import SynthDID
from synthdid.sample_data import fetch_CaliforniaSmoking
df = fetch_CaliforniaSmoking()
PRE_TEREM = [1970, 1988] POST_TEREM = [1989, 2000]
TREATMENT = ["California"]
- estimation & plot
```python
sdid = SynthDID(df, PRE_TEREM, POST_TEREM, TREATMENT)
sdid.fit(zeta_type="base")
sdid.plot(model="sdid")
Details of each method will be created later.
notebook
for basic usageReproductionExperiment_CaliforniaSmoking.ipynb
OtherOmegaEstimationMethods.ipynb
omega
(& zeta
). As a result, it confirms the robustness of the estimation method in the original paper.ScaleTesting_of_DonorPools.ipynb