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Poster
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
Afsaneh Mastouri · Yuchen Zhu · Limor Gultchin · Anna Korba · Ricardo Silva · Matt J. Kusner · Arthur Gretton · Krikamol Muandet

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 3 »

We address the problem of causal effect estima-tion in the presence of unobserved confounding,but where proxies for the latent confounder(s) areobserved. We propose two kernel-based meth-ods for nonlinear causal effect estimation in thissetting: (a) a two-stage regression approach, and(b) a maximum moment restriction approach. Wefocus on the proximal causal learning setting, butour methods can be used to solve a wider classof inverse problems characterised by a Fredholmintegral equation. In particular, we provide a uni-fying view of two-stage and moment restrictionapproaches for solving this problem in a nonlin-ear setting. We provide consistency guaranteesfor each algorithm, and demonstrate that these ap-proaches achieve competitive results on syntheticdata and data simulating a real-world task. In par-ticular, our approach outperforms earlier methodsthat are not suited to leveraging proxy variables.

Keywords: Kernel Methods · Algorithms

Author Information

Afsaneh Mastouri (University College London)
Yuchen Zhu (University College London)
Limor Gultchin (University of Oxford)
Anna Korba (CREST/ENSAE)
Ricardo Silva (University College London)
Matt J. Kusner (University College London)
Arthur Gretton (Gatsby Computational Neuroscience Unit)
Krikamol Muandet (Max Planck Institute for Intelligent Systems)

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