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Neural Score Matching for High-Dimensional Causal Inference

Clivio, Oscar; Falck, Fabian; Lehmann, Brieuc; Deligiannidis, George; Holmes, Chris; (2022) Neural Score Matching for High-Dimensional Causal Inference. In: Camps-Valls, G and Ruiz, FJR and Valera, I, (eds.) Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. (pp. pp. 1-35). PMLR Green open access

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Abstract

Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact matching find exponentially fewer matches as the input dimension grows, and propensity score matching may match highly unrelated units together. To overcome this problem, we develop theoretical results which motivate the use of neural networks to obtain non-trivial, multivariate balancing scores of a chosen level of coarseness, in contrast to the classical, scalar propensity score. We leverage these balancing scores to perform matching for high-dimensional causal inference and call this procedure neural score matching. We show that our method is competitive against other matching approaches on semi-synthetic high-dimensional datasets, both in terms of treatment effect estimation and reducing imbalance

Type: Proceedings paper
Title: Neural Score Matching for High-Dimensional Causal Inference
Event: The 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022
Location: Valencia, Spain
Dates: 28th-30th March 2022
Open access status: An open access version is available from UCL Discovery
Publisher version: https://proceedings.mlr.press/v151/clivio22a/clivi...
Language: English
Additional information: © The Author 2022. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10158921
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