Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
Methodology
2020-08-11 v2 Machine Learning
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.
Cite
@article{arxiv.2003.01805,
title = {Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation},
author = {Marco Morucci and Vittorio Orlandi and Sudeepa Roy and Cynthia Rudin and Alexander Volfovsky},
journal= {arXiv preprint arXiv:2003.01805},
year = {2020}
}