Step by step Python code for applying synthetic control to interesting use cases
This repo will contain many examples for using the synthetic control method in diverse domains. Meanwhile, these are the implemented examples:
-
Estimating the Effect of California’s Tobacco Control Program (Abadie et al. 2010)
The relevant notebook is
Proposition99.ipynb
Data sources for this use case are:
- 'smoking.rda' taken from https://github.com/johnson-shuffle/mixtape/tree/master/data
- 'prop99.csv' taken from https://github.com/jehangiramjad/tslib/tree/master/tests/testdata
The notebook goes through all preprocessing and processing steps to yield the figures just like in the original 2010 paper:
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