تقرير
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
| العنوان: | Automated Efficient Estimation using Monte Carlo Efficient Influence Functions |
|---|---|
| المؤلفون: | Agrawal, Raj, Witty, Sam, Zane, Andy, Bingham, Eli |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Computation, Computer Science - Machine Learning, Statistics - Methodology |
| الوصف: | Many practical problems involve estimating low dimensional statistical quantities with high-dimensional models and datasets. Several approaches address these estimation tasks based on the theory of influence functions, such as debiased/double ML or targeted minimum loss estimation. This paper introduces \textit{Monte Carlo Efficient Influence Functions} (MC-EIF), a fully automated technique for approximating efficient influence functions that integrates seamlessly with existing differentiable probabilistic programming systems. MC-EIF automates efficient statistical estimation for a broad class of models and target functionals that would previously require rigorous custom analysis. We prove that MC-EIF is consistent, and that estimators using MC-EIF achieve optimal $\sqrt{N}$ convergence rates. We show empirically that estimators using MC-EIF are at parity with estimators using analytic EIFs. Finally, we demonstrate a novel capstone example using MC-EIF for optimal portfolio selection. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.00158 |
| رقم الأكسشن: | edsarx.2403.00158 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |