Conditional expectation estimation through attributable components
Abstract
A general methodology is proposed for the explanation of variability in a quantity of interest x in terms of covariates z = (z1, …, zL). It provides the conditional mean $\bar{x}(z)$ as a sum of components, where each component is represented as a product of non-parametric one-dimensional functions of each covariate zl that are computed through an alternating projection procedure. Both x and the zl can be real or categorical variables; in addition, some or all values of each zl can be unknown, providing a general framework for multi-clustering, classification and covariate imputation in the presence of confounding factors.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 15, 2018
- Source ID
- 10.1093/imaiai/iax023
Entities
People
- Esteban G. Tabak
- Giulio Trigila
Organizations
- Baruch College
- Courant Institute of Mathematical Sciences, NYU
- Office of Naval Research