I have posted a couple of blogs on the powerful technique of (multidimensional) Robust Synthetic Control here and here . In this post I will give a short tutorial on how you can use mRSC to perform your own analysis using the python package my collaborator Jehangir has made available on github. This posting will be about counterfactual analysis. We will work with the canonical example of the synthetic control based counterfactual analysis of the impact California's Prop 99 . All the data and code is included in the github repository linked above. I will post the python code as run on a Jupyter Notebook, and the "tslib" library referenced above has been downloaded and is available. Preliminaries: importing the libraries. In [1]: import sys , os sys . path . append ( "../.." ) sys . path . append ( ".." ) sys . path . append ( os . getcwd ()) from matplotlib import pyplot as plt import matplotlib.ticker as ti
