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Tangential Wasserstein Projections

Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee

arXiv 29 Jul 2022 · Statistics — Machine Learning

arXiv:2207.14727 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We develop a notion of projections between sets of probability measures using the geometric properties of the 2-Wasserstein space. It is designed for general multivariate probability measures, is computationally efficient to implement, and provides a unique solution in regular settings. The idea is to work on regular tangent cones of the Wasserstein space using generalized geodesics. Its structure and computational properties make the method applicable in a variety of settings, from causal inference to the analysis of object data. An application to estimating causal effects yields a generalization of the notion of synthetic controls to multivariate data with individual-level heterogeneity, as well as a way to estimate optimal weights jointly over all time periods.

Citation extraction

74
references
131
in-text mentions
74
distinct cited
2
self-citations
7,741
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Abadie, A. and Gardeazabal, J (2003) The economic costs of conflict: A case study of the basque country0.92843100%
2Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program0.92843100%
3Ambrosio, L., Gigli, N., and Savaré, G (2008) Gradient flows in metric spaces and in the space of probability measures0.81920355%
4Bonneel, N., Peyré, G., and Cuturi, M (2016) Wasserstein barycentric coordinates: histogram regression using optimal transport0.81142100%
5Villani, C (2003) Topics in optimal transportation0.7373367%
6Zemel, Y. and Panaretos, V. M (2019) Fréchet means and procrustes analysis in wasserstein space0.7373367%
7Abadie, A (2021) Using synthetic controls: Feasibility, data requirements, and methodological aspects0.73732100%
8Gunsilius, F (2022) Distributional synthetic controls self0.73732100%
9Peyré, G. and Cuturi, M (2019) Computational optimal transport0.73732100%
10Agueh, M. and Carlier, G (2011) Barycenters in the Wasserstein Space0.64441100%

Showing the top 10 of 74 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Compositional Synthetic Controls0.40511