Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1089-1098, 2020.

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 the causal effect for each unit.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-morucci20a, title = {Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation}, author = {Morucci, Marco and Orlandi, Vittorio and Roy, Sudeepa and Rudin, Cynthia and Volfovsky, Alexander}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {1089--1098}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/morucci20a/morucci20a.pdf}, url = {https://proceedings.mlr.press/v124/morucci20a.html}, 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 the causal effect for each unit. } }
Endnote
%0 Conference Paper %T Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation %A Marco Morucci %A Vittorio Orlandi %A Sudeepa Roy %A Cynthia Rudin %A Alexander Volfovsky %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-morucci20a %I PMLR %P 1089--1098 %U https://proceedings.mlr.press/v124/morucci20a.html %V 124 %X 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 the causal effect for each unit.
APA
Morucci, M., Orlandi, V., Roy, S., Rudin, C. & Volfovsky, A.. (2020). Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:1089-1098 Available from https://proceedings.mlr.press/v124/morucci20a.html.

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