Math @ Duke
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Publications [#359954] of Cynthia D. Rudin
Papers Published
- Gupta, NR; Orlandi, V; Chang, C-R; Wang, T; Morucci, M; Dey, P; Howell, TJ; Sun, X; Ghosal, A; Roy, S; Rudin, C; Volfovsky, A, dame-flame: A Python Library Providing Fast Interpretable Matching for
Causal Inference, vol. abs/2101.01867
(January, 2021)
(last updated on 2024/04/23)
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/
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