SplitUP estimator (arXiv:2601.15254) repro pkg see also DOI: 10.13140/RG.2.2.20833.77925
1.0K
Monte Carlo simulations for the SplitUP estimator from:
"Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?" (arXiv:2601.15254)
SplitUP is a novel estimator for causal inference when treatment (X) and outcome (Y) are never jointly observed - a common scenario in genomics, economics, and medical research where data linkage is
impossible due to privacy or technical constraints.
| Estimator | Description |
|---|---|
| NaiveOLS | Baseline ordinary least squares (biased) |
| TS-IV | Two-stage instrumental variable estimator |
| UP-GMM | Unpaired generalized method of moments |
| SplitUP | Cross-fold bias-corrected estimator (proposed) |
docker pull pageman/splitup-reproducibility:latest
docker run -p 8888:8888 pageman/splitup-reproducibility:latest
Then open http://localhost:8888 in your browser to access Jupyter Lab.
Key Features
- Full Python implementation of all estimators from the paper
- Configurable simulation parameters (m, n, r, d, sparsity)
- Reproducible Monte Carlo experiments
- Visualization of estimator performance
Environment
- Python 3.12.2
- PyTorch 2.9.1
- NumPy 1.26.4
- SciPy 1.16.2
- Scikit-learn 1.7.2
Tags
- latest - Most recent build
- 1.1.0 - Stable release
Citations
@misc{schur2026experimentsrepetitionsunpaireddata,
title={Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?},
author={Felix Schur and Niklas Pfister and Peng Ding and Sach Mukherjee and Jonas Peters},
year={2026},
eprint={2601.15254},
archivePrefix={arXiv},
primaryClass={stat.ML},
url={https://arxiv.org/abs/2601.15254}
}
@article{pajo2026splitup,
author={Pajo, Paul},
title={Finite-Sample Performance of SplitUP in Many-Environments Unpaired Instrumental Variables: A Simulation Study with Pilot Results},
month={January},
year={2026},
doi={10.13140/RG.2.2.20833.77925}
}
License
MIT License - Free for academic and research use.
Content type
Image
Digest
sha256:15c919024…
Size
447.2 MB
Last updated
8 months ago
docker pull pageman/splitup-reproducibility