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, 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 effects 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/Comment: 5 pages, 1 figure; Reference and discussion of CEM correcte