تقرير
Tangential Wasserstein Projections
| العنوان: | Tangential Wasserstein Projections |
|---|---|
| المؤلفون: | Gunsilius, Florian, Hsieh, Meng Hsuan, Lee, Myung Jin |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Economics - Econometrics, Mathematics - Statistics Theory |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2207.14727 |
| رقم الأكسشن: | edsarx.2207.14727 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |