dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference
Abstract
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, and high-quality, because machine learning is used to determine which covariates are important to match on. DAME solves an optimization problem that matches units on as many covariates as possible, prioritizing matches on important covariates. FLAME approximates the solution found by DAME via a much faster backward feature selection procedure. The package provides several adjustable parameters to adapt the algorithms to specific applications, and can calculate treatment effect estimates after matching. Descriptions of these parameters, details on estimating treatment effects, and further examples, can be found in the documentation at https://almost-matching-exactly.github.io/DAME-FLAME-Python-Package/
Keywords
Cite
@article{arxiv.2101.01867,
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 preprint arXiv:2101.01867},
year = {2023}
}
Comments
26 pages, 2 figures