Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.
Abstract
We propose a matching method for observational data that matches units with
others in unit-specific, hyper-box-shaped regions of the covariate space. These
regions are large enough that many matches are created for each unit and small
enough that the treatment effect is roughly constant throughout. The regions
are found as either the solution to a mixed integer program, or using a (fast)
approximation algorithm. The result is an interpretable and tailored estimate
of a causal effect for each unit.
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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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