Neural Score Matching for High-Dimensional Causal Inference

• URL: http://arxiv.org/abs/2203.00554v1
• Date: Tue, 1 Mar 2022 15:36:12 GMT
• Title: Neural Score Matching for High-Dimensional Causal Inference
• Authors: Oscar Clivio, Fabian Falck, Brieuc Lehmann, George Deligiannidis, Chris Holmes
• Abstract summary: We develop theoretical results which motivate the use of neural networks to obtain non-trivial balancing scores of a chosen level of coarseness. We show that our method is competitive against other matching approaches on semi-synthetic high-dimensional datasets.
• Score: 5.696039065328919
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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