GDR-Learners: Orthogonal Learning of Generative Models for Potential Outcomes
MCML Authors
Valentyn Melnychuk
Stefan Feuerriegel
Prof. Dr.
Principal Investigator
Abstract
Valentyn Melnychuk
Stefan Feuerriegel
Prof. Dr.
Principal Investigator
Abstract
Various deep generative models have been proposed to estimate potential outcomes distributions from observational data. However, none of them have the favorable theoretical property of general Neyman-orthogonality and, associated with it, quasi-oracle efficiency and double robustness. In this paper, we introduce a general suite of generative Neyman-orthogonal (doubly-robust) learners that estimate the conditional distributions of potential outcomes. Our proposed GDR-learners are flexible and can be instantiated with many state-of-the-art deep generative models. In particular, we develop GDR-learners based on (a) conditional normalizing flows (which we call GDR-CNFs), (b) conditional generative adversarial networks (GDR-CGANs), (c) conditional variational autoencoders (GDR-CVAEs), and (d) conditional diffusion models (GDR-CDMs). Unlike the existing methods, our GDR-learners possess the properties of quasi-oracle efficiency and rate double robustness, and are thus asymptotically optimal. In a series of (semi-)synthetic experiments, we demonstrate that our GDR-learners are very effective and outperform the existing methods in estimating the conditional distributions of potential outcomes.
misc
Preprint
Sep. 2025Authors
V. Melnychuk • S. FeuerriegelLinks
Research Area
BibTeXKey: MF25