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

https://medium.com/@masa_asami/causal-inference-using-synthetic-difference-in-differences-with-python-5758e5a76909

$ 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.

• Refactoring and better documentation
• Completion of the TEST code

• setup

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

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.

• ReproductionExperiment_CaliforniaSmoking.ipynb

  • This is a reproduction experiment note of the original paper, using a famous dataset (CaliforniaSmoking).

• OtherOmegaEstimationMethods.ipynb

  • This note is a different take on the estimation method for parameter omega (& zeta ). As a result, it confirms the robustness of the estimation method in the original paper.

• ScaleTesting_of_DonorPools.ipynb

  • In this note, we will check how the estimation results change with changes in the scale of the donor pool features.
  • Adding donor pools with extremely different scales (e.g., 10x) can have a significant impact (bias) on the estimates.
  • If different scales are mixed, as is usually the case in traditional regression, preprocessing such as logarithmic transformation is likely to be necessary

• This module is still under development.
• If you have any questions or comments, please feel free to use issues.