AME: Interpretable Almost Exact Matching for Causal Inference

Haoning Jiang, Thomas Howell, Neha R. Gupta, Vittorio Orlandi, Marco Morucci, Harsh Parikh, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR 176:355-359, 2022.

Abstract

AME-GUI (Almost Matching Exactly Graphical User Interface) is an interactive web- based application that allows users to perform matching for causal inference on large, complex datasets with categorical covariates. The application is powered by the Fast Large-Scale Almost Matching Exactly (FLAME) algorithm (Wang et al., 2021), which matches treatment and control units in a way that is (i) interpretable, because the matches are made directly on covariates, (ii) high-quality, because machine learning is used to determine which covariates are important to match on, and (iii) scalable, using techniques from data management. The graphical user interface highlights the utility of this algorithm and uses a suite of visualization tools to facilitate easy and interactive exploration of treatment effect estimates, as well as of the created matched groups that they depend on. The application gives a quick and simple overview of the open-source Python and R packages dame-flame and FLAME, and the range of functionality they provide for interpretable and efficient causal inference.

Cite this Paper

BibTeX
@InProceedings{pmlr-v176-jiang22b, title = {AME: Interpretable Almost Exact Matching for Causal Inference}, author = {Jiang, Haoning and Howell, Thomas and Gupta, Neha R. and Orlandi, Vittorio and Morucci, Marco and Parikh, Harsh and Roy, Sudeepa and Rudin, Cynthia and Volfovsky, Alexander}, booktitle = {Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track}, pages = {355--359}, year = {2022}, editor = {Kiela, Douwe and Ciccone, Marco and Caputo, Barbara}, volume = {176}, series = {Proceedings of Machine Learning Research}, month = {06--14 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v176/jiang22b/jiang22b.pdf}, url = {https://proceedings.mlr.press/v176/jiang22b.html}, abstract = {AME-GUI (Almost Matching Exactly Graphical User Interface) is an interactive web- based application that allows users to perform matching for causal inference on large, complex datasets with categorical covariates. The application is powered by the Fast Large-Scale Almost Matching Exactly (FLAME) algorithm (Wang et al., 2021), which matches treatment and control units in a way that is (i) interpretable, because the matches are made directly on covariates, (ii) high-quality, because machine learning is used to determine which covariates are important to match on, and (iii) scalable, using techniques from data management. The graphical user interface highlights the utility of this algorithm and uses a suite of visualization tools to facilitate easy and interactive exploration of treatment effect estimates, as well as of the created matched groups that they depend on. The application gives a quick and simple overview of the open-source Python and R packages dame-flame and FLAME, and the range of functionality they provide for interpretable and efficient causal inference.} }
Endnote
%0 Conference Paper %T AME: Interpretable Almost Exact Matching for Causal Inference %A Haoning Jiang %A Thomas Howell %A Neha R. Gupta %A Vittorio Orlandi %A Marco Morucci %A Harsh Parikh %A Sudeepa Roy %A Cynthia Rudin %A Alexander Volfovsky %B Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track %C Proceedings of Machine Learning Research %D 2022 %E Douwe Kiela %E Marco Ciccone %E Barbara Caputo %F pmlr-v176-jiang22b %I PMLR %P 355--359 %U https://proceedings.mlr.press/v176/jiang22b.html %V 176 %X AME-GUI (Almost Matching Exactly Graphical User Interface) is an interactive web- based application that allows users to perform matching for causal inference on large, complex datasets with categorical covariates. The application is powered by the Fast Large-Scale Almost Matching Exactly (FLAME) algorithm (Wang et al., 2021), which matches treatment and control units in a way that is (i) interpretable, because the matches are made directly on covariates, (ii) high-quality, because machine learning is used to determine which covariates are important to match on, and (iii) scalable, using techniques from data management. The graphical user interface highlights the utility of this algorithm and uses a suite of visualization tools to facilitate easy and interactive exploration of treatment effect estimates, as well as of the created matched groups that they depend on. The application gives a quick and simple overview of the open-source Python and R packages dame-flame and FLAME, and the range of functionality they provide for interpretable and efficient causal inference.
APA
Jiang, H., Howell, T., Gupta, N.R., Orlandi, V., Morucci, M., Parikh, H., Roy, S., Rudin, C. & Volfovsky, A.. (2022). AME: Interpretable Almost Exact Matching for Causal Inference. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, in Proceedings of Machine Learning Research 176:355-359 Available from https://proceedings.mlr.press/v176/jiang22b.html.

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