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A Neural Mean Embedding Approach for Back-door and Front-door Adjustment

Xu, Liyuan; Gretton, Arthur; (2023) A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. In: Proceedings of the Eleventh International Conference on Learning Representations. (pp. p. 2756). International Conference on Learning Representations: Kigali, Rwanda. (In press). Green open access

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Abstract

We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment. The goal in both cases is to recover the treatment effect without having an access to a hidden confounder. This objective is attained by first estimating the conditional mean of the desired outcome variable given relevant covariates (the “first stage” regression), and then taking the (conditional) expectation of this function as a “second stage” procedure. We propose to compute these conditional expectations directly using a regression function to the learned input features of the first stage, thus avoiding the need for sampling or density estimation. All functions and features (and in particular, the output features in the second stage) are neural networks learned adaptively from data, with the sole requirement that the final layer of the first stage should be linear. The proposed method is shown to converge to the true causal parameter, and outperforms the recent state-of-the-art methods on challenging causal benchmarks, including settings involving high-dimensional image data.

Type: Proceedings paper
Title: A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
Event: The Eleventh International Conference on Learning Representations
Location: Kigali Rwanda
Dates: 1 May 2023 - 5 May 2023
Open access status: An open access version is available from UCL Discovery
Publisher version: https://iclr.cc/public/JournalToConference
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10166305
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