README.md

synthlearners: Scalable Synthetic Control Methods in Python

synthetic control methods powered by the pyensmallen library for fast optimisation. Check out the notebooks directory for synthetic and real data examples.

installation

pip install git+https://github.com/apoorvalal/synthlearners/

or git clone and run uv pip install -e . and make changes.

features

features are indicated by

• pending; good first PR; contributions welcome
• done

weights

• unit weights [/solvers.py]
  • simplex (Abadie, Diamond, Hainmueller 2010, 2015)
  • lasso (Hollingsworth and Wing 2024+)
  • ridge (Imbens and Doudchenko 2016, Arkhangelsky et al 2021)
  • matching (Imai, Kim, Wang 2023)
  • support intercept term (Ferman and Pinto 2021, Doudchenko and Imbens)
  • entropy weights (Hainmueller 2012, Hirschberg and Arkhangelsky 2023, Lal 2023)
• with multiple treated units, match aggregate outcomes (default) or individual outcomes (Abadie and L'Hour 2021)
• time weights
  • L2 weights (Arkhangelsky et al 2021)
  • time-distance penalised weights (Imbens et al 2024)
• augmenting weights with outcome models (Ben-Michael et al 2021)
  • matrix completion (Athey et al 2021)
  • latent factor models (Xu 2017, Lal et al 2024)
  • two-way kernel ridge weights (Ben-Michael et al 2023)

inference

• jacknife confidence intervals (multiple treated units) [Arkhangelsky et al 2021)
• permutation test (Abadie et al 2010)
• conformal inference (Chernozhukov et al 2021)

visualisations

• raw outcome time series with treated average and synthetic control
• event study plot (treatment effect over time)
• weight distributions

Contributions welcome!