https://github.com/apoorvalal/synthlearners
fast synthetic control estimators for panel data problems
https://github.com/apoorvalal/synthlearners
causal-inference econometrics panel-data synthetic-control
Last synced: about 1 year ago
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fast synthetic control estimators for panel data problems
- Host: GitHub
- URL: https://github.com/apoorvalal/synthlearners
- Owner: apoorvalal
- Created: 2024-11-12T01:49:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-12T14:41:29.000Z (about 1 year ago)
- Last Synced: 2025-04-12T23:38:17.281Z (about 1 year ago)
- Topics: causal-inference, econometrics, panel-data, synthetic-control
- Language: Jupyter Notebook
- Homepage:
- Size: 23.6 MB
- Stars: 19
- Watchers: 2
- Forks: 2
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
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README
# `synthlearners`: Scalable Synthetic Control Methods in Python
synthetic control methods powered by the [`pyensmallen`](https://github.com/apoorvalal/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
- [x] done
### weights
- [x] unit weights [`/solvers.py`]
- [x] simplex (Abadie, Diamond, Hainmueller [2010](https://www.tandfonline.com/doi/abs/10.1198/jasa.2009.ap08746?casa_token=HHoPpXX1iigAAAAA:zCB_ZwLLTs1uWBzAVrwgCKtA_FPZXdoqLoxKgZzGAvCCgLpA5WlFm4DphUiz2U_udE5GM329XdjWoQ), [2015](https://onlinelibrary.wiley.com/doi/full/10.1111/ajps.12116?casa_token=bKtsjsYAkAIAAAAA%3AuS7vADpexw4q0BACgWtaYDal1fwCI3k3bHruSUgCJyEVs_PrUlnmcenEK58f6QoqgCPBgZGTy0mssg))
- [x] lasso ([Hollingsworth and Wing 2024+](https://osf.io/fc9xt/))
- [x] ridge ([Imbens and Doudchenko 2016](https://www.nber.org/papers/w22791), [Arkhangelsky et al 2021](https://www.aeaweb.org/articles?id=10.1257/aer.20190159))
- [x] matching ([Imai, Kim, Wang 2023](https://onlinelibrary.wiley.com/doi/full/10.1111/ajps.12685?casa_token=vap307wR7DwAAAAA%3AHGX_puzkDArA-O-mTfxOedqsr1zdVH4VgwgBA8pi8LnzUg1IVVUHEeVrIcCZZ1gA7gfqsrebAgIEJg))
- [x] support intercept term ([Ferman and Pinto 2021](https://onlinelibrary.wiley.com/doi/abs/10.3982/QE1596), Doudchenko and Imbens)
- [ ] entropy weights ([Hainmueller 2012](https://www.cambridge.org/core/journals/political-analysis/article/entropy-balancing-for-causal-effects-a-multivariate-reweighting-method-to-produce-balanced-samples-in-observational-studies/220E4FC838066552B53128E647E4FAA7), [Hirschberg and Arkhangelsky 2023](https://arxiv.org/abs/2311.13575), [Lal 2023](https://apoorvalal.github.io/files/papers/augbal.pdf))
- [x] with multiple treated units, match aggregate outcomes (default) or individual outcomes ([Abadie and L'Hour 2021](https://economics.mit.edu/sites/default/files/publications/A%20Penalized%20Synthetic%20Control%20Estimator%20for%20Disagg.pdf))
- [ ] 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](https://arxiv.org/abs/1811.04170))
- [x] matrix completion ([Athey et al 2021](https://arxiv.org/abs/1710.10251))
- [ ] latent factor models ([Xu 2017](https://yiqingxu.org/papers/english/2016_Xu_gsynth/Xu_PA_2017.pdf), Lal et al 2024)
- [ ] two-way kernel ridge weights ([Ben-Michael et al 2023](https://arxiv.org/abs/2110.07006))
### inference
- [x] jacknife confidence intervals (multiple treated units) [Arkhangelsky et al 2021)
- [x] permutation test (Abadie et al 2010)
- [ ] conformal inference ([Chernozhukov et al 2021](https://arxiv.org/abs/1712.09089))
### visualisations
- [x] raw outcome time series with treated average and synthetic control
- [x] event study plot (treatment effect over time)
- [x] weight distributions
Contributions welcome!