Orthogonal Representation Learning for Estimating Causal Quantities

Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal, Stefan Feuerriegel

Keywords: treatment effect estimation, counterfactual outcomes estimation, representation learning

TL;DR: We propose a novel class of Neyman-orthogonal learners for causal quantities defined at the representation level

Abstract: Representation learning is widely used for estimating causal quantities (e.g., the conditional average treatment effect) from observational data. While existing representation learning methods have the benefit of allowing for end-to-end learning, they do not have favorable theoretical properties of Neyman-orthogonal learners, such as double robustness and quasi-oracle efficiency. Also, such representation learning methods often employ additional constraints, like balancing, which may even lead to inconsistent estimation. In this paper, we propose a novel class of Neyman-orthogonal learners for causal quantities defined at the representation level, which we call OR-learners. Our OR-learners have several practical advantages: they allow for consistent estimation of causal quantities based on any learned representation, while offering favorable theoretical properties including double robustness and quasi-oracle efficiency. In numerous experiments, we show that, under certain regularity conditions, our OR-learners improve existing representation learning methods and achieve state-of-the-art performance. To the best of our knowledge, our OR-learners are the first work to provide a unified framework of representation learning methods and Neyman-orthogonal learners for causal quantities estimation.

Primary Area: causal reasoning

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Submission Number: 3983