This is the repository containing the code for the Python library mlsynth. I provide detailed tutorials for each estimator in the package. As of May 24, 2024, we have the two-step SCM, linking to my Forward DID package (which will someday be moved here and integrated), as well as the Principal Component Regression estimator and Robust PCA SCM. One day, I will learn how to make the full thing installable from here to facilitate use, but for now I provide the code piece by piece until it is finished.
To use mlsynth, at minimum you need a Python dataframe with
- One column for the Unit ID (these must be unique, but they may be string values or numerical values).
- One column for time (indexed to the unit variable, these must form a balanced panel).
- A numerical outcome column of interest, and
- One column that is an indicator denoting treatment status. At this time,
mlsynthonly accepts binary interventions and one treated unit per df (this does NOT MEAN that you can't use the current functionality in settings of staggered adoption, but it does mean that you, the user, must be mindful about things like event-time and averaging your ATTs accordingly). In time, staggered adoption settings will come.
In addition, you must have standard libraries installed, namely cvxpy for optimization, scipy, numpy, pandas, matplotlib, and sklearn.