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 (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/
Type
Journal articlePermalink
https://hdl.handle.net/10161/22495Collections
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Sudeepa Roy
Associate Professor of Computer Science
I joined the Department of Computer Science at Duke University in Fall 2015.
Before joining Duke, I was a postdoctoral research associate in the Department of
Computer Science and Engineering,University of Washington where I worked with Prof.
Dan Suciu and the database group.
I graduated from the Uni
Cynthia D. Rudin
Earl D. McLean, Jr. Professor
Cynthia Rudin is a professor of computer science, electrical and computer engineering,
statistical science, and biostatistics & bioinformatics at Duke University, and directs
the Interpretable Machine Learning Lab. Previously, Prof. Rudin held positions at
MIT, Columbia, and NYU. She holds an undergraduate degree from the University at Buffalo,
and a PhD from Princeton University. She is the recipient of the 2022 Squirrel AI
Award for Artificial Intelligence for the Benefit of Human
Alexander Volfovsky
Assistant Professor of Statistical Science
I am interested in theory and methodology for network analysis, causal inference and
statistical/computational tradeoffs and in applications in the social sciences. Modern
data streams frequently do not follow the traditional paradigms of n independent observations
on p quantities of interest. They can include complex dependencies among the observations
(e.g. interference in the study of causal effects) or among the quantities of interest
(e.g. probabilities of edge formation in a network). My r
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