• 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… 
View PDF on arXiv
View With Semantic Reader
1 Citations

Figures and Tables from this paper

One Citation

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.

References

SHOWING 1-6 OF 6 REFERENCES
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.
Interpretable Almost-Exact Matching for Causal Inference
TLDR
Notable advantages of the method over existing matching procedures are its high-quality interpretable matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
TLDR
MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions.
MICE: Multivariate Imputation by Chained Equations in R
TLDR
Mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs.
cem: Software for Coarsened Exact Matching
TLDR
The program implements the coarsened exact matching (CEM) algorithm, described below, which may be used alone or in combination with any existing matching method.
DoWhy: A Python package for causal inference
  • https://github.com/microsoft/dowhy,
  • 2019