• Corpus ID: 230770166

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference

@article{Gupta2021dameflameAP,
  title={dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference},
  author={Neha R. Gupta and Vittorio Orlandi and Chia-Rui Chang and Tianyu Wang and Marco Morucci and Pritam Dey and Thomas J. Howell and Xian Sun and Angikar Ghosal and Sudeepa Roy and Cynthia Rudin and Alexander Volfovsky},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.01867}
}
dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the Dynamic Almost Matching Exactly (DAME) and Fast, Large-Scale Almost Matching Exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made on covariates (rather than, for instance, propensity scores), and high-quality… 
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Figures and Tables from this paper

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
TLDR
This work proposes a method that computes high quality almost-exact matches for high-dimensional categorical datasets, and leverages techniques that are natural for query processing in the area of database management to perform matching efficiently for large datasets.

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This work proposes a method that computes high quality almost-exact matches for high-dimensional categorical datasets, and leverages techniques that are natural for query processing in the area of database management to perform matching efficiently for large datasets.
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